Publications

Topics:

Computational Imaging & Optics

Signal & Image Processing

Computer Vision

Medical Imaging & Data Analysis

3D Vision & Numerical Geometry

Deep Learning Algorithms & Hardware

Large Scale Data Analysis

Bioinformatics & Computational Chemistry

All Journal Conferences Books Chapters Reports Patents Misc

M. M. Bronstein, A. M. Bronstein, Biometrics was no match for hair-raising tricks, Nature Vol. 420, 2002
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, H. Azhari, Reconstruction in ultrasound diffraction tomography using non-uniform FFT, IEEE Trans. on Medical Imaging, Vol. 21(11), 2002 details
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Iterative reconstruction in diffraction tomography using non-uniform fast Fourier transform, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2002 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Optimal nonlinear estimation of photon coordinates in PET, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2002 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Optimal nonlinear line-of-flight estimation in positron emission tomography, IEEE Trans. on Nuclear Science, Vol. 50(3), 2003 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Separation of semireflective layers using Sparse ICA, Proc. Int'l Conf. on Acoustics Speech and Signal Processing (ICASSP), 2003 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-invariant 3D face recognition, Proc. Audio- and Video-based Biometric Person Authentication (AVBPA), Lecture Notes in Comp. Science No. 2688, Springer, 2003 details
A. M. Bronstein, M. M. Bronstein, E. Gordon, R. Kimmel, Fusion of 2D and 3D data in three-dimensional face recognition, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,, Optimal sparse representations for blind source separation and blind deconvolution: a learning approach, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Fast relative Newton algorithm for blind deconvolution of images, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, A. Spira, Face recognition from facial surface metric, Proc. European Conf. on Computer Vision (ECCV), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Blind source separation using block-coordinate relative Newton method, Signal Processing, Vol. 84(8), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Blind source separation using the block-coordinate relative Newton method, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, Lecture Notes in Comp. Science No. 3195, Springer, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, QML blind deconvolution: asymptotic analysis, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Optimal sparse representations for blind deconvolution of images, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Quasi maximum likelihood blind deconvolution of images acquired through scattering media, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2004 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Three-dimensional face recognition, Int'l Journal of Computer Vision (IJCV), Vol. 64(1), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Quasi maximum likelihood blind deconvolution: super- an sub-Gaussianity versus consistency, IEEE Trans. Signal Processing, Vol. 53(7), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Relative optimization for blind deconvolution, IEEE Trans. on Signal Processing, Vol. 53(6), 2005 details
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Blind deconvolution of images using optimal sparse representations, IEEE Trans. on Image Processing, Vol. 14(6), 2005 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-invariant face recognition via spherical embedding, Proc. Int'l Conf. on Image Processing (ICIP), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Unmixing tissues: sparse component analysis in multi-contrast MRI, Proc. Int'l Conf. on Image Processing (ICIP), 2005 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Isometric embedding of facial surfaces into S^3, Proc. Int'l Conf. on Scale Space and PDE Methods in Computer Vision (SSVM), 2005 details
M. M. Bronstein, A. M. Bronstein, R. Kimmel, I. Yavneh, A multigrid approach for multi-dimensional scaling, Proc. Copper Mountain Conf. Multigrid Methods, 2005 (Best Paper Award) details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Sparse ICA for blind separation of transmitted and reflected images, Int'l Journal of Imaging Science and Technology (IJIST), Vol. 15(1), 2005 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Robust expression-invariant face recognition from partially missing data, Proc. European Conf. on Computer Vision (ECCV), 2006 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Matching two-dimensional articulated shapes using generalized multidimensional scaling, Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Face2Face: an isometric model for facial animation, Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Efficient computation of isometry-invariant distances between surfaces, SIAM J. Scientific Computing, Vol. 28(5), 2006
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, On separation of semitransparent dynamic images from static background, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2006 details
M. M. Bronstein, A. M. Bronstein, R. Kimmel, I. Yavneh, Multigrid multidimensional scaling, Numerical Linear Algebra with Applications (NLAA), Vol. 13(2), 2006 (Special issue on multigrid methods) details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching, Proc. US National Academy of Sciences (PNAS), Vol. 103(5), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression invariant face recognition: faces as isometric surfaces, Chapter in Face Processing: Advanced Modeling and Methods (Rama Chellappa, Wenyi Zhao Eds.), Academic Press, 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Calculus of non-rigid surfaces for geometry and texture manipulation, IEEE Trans. Visualization and Computer Graphics, Vol 13(5), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes, Proc. Int'l Conf. Computer Vision (ICCV), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Weighted distance maps computation on parametric three-dimensional manifolds, Journal of Computational Physics, Vol. 255(1), 2007 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Paretian similarity for partial comparison of non-rigid objects, Proc. Scale Space and Variational Methods in Computer Vision (SSVM), 2007 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Partial similarity of objects and text sequences, Proc. Information Theory and Applications Workshop, 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-invariant representation of faces, IEEE Trans. Image Processing, Vol. 16(1), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Story of Cinderella: biometrics and isometry-invariant distances, Chapter in 3D Imaging for Safety and Security (A. Koschan, M. Pollefeys, M. Abidi Eds.), Springer, 2007 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Symmetries of non-rigid shapes, Proc. Workshop on Non-rigid Registration and Tracking through Learning (NRTL), 2007 details
O. Weber, Y. Devir, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Parallel algorithms for approximation of distance maps on parametric surfaces, ACM Trans. on Graphics, Vol. 27(4), 2008 details
A. M. Bronstein, M. M. Bronstein, Regularized partial matching of rigid shapes, Proc. European Conf. on Computer Vision (ECCV), 2008 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Analysis of two-dimensional non-rigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 78(1), 2008 details
A. M. Bronstein, M. M. Bronstein, Not only size matters: regularized partial matching of nonrigid shapes, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Registration (NORDIA), 2008 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Numerical geometry of non-rigid shapes, Springer, 2008, ISBN: 978-0387733005 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Topologically constrained isometric embedding, Human Motion Understanding, Modeling, Capture, and Animation, Computational Imaging and Vision, Vol. 36, Springer, 2008 details
R. Giryes, A. M. Bronstein, Y. Moshe, M. M. Bronstein, Embedded system for 3D shape reconstruction, Proc. European DSP Education and Research Symposium (EDERS), 2008 details
O. Rubinstein, Y. Honen, A. M. Bronstein, M. M. Bronstein, R. Kimmel, 3D color video camera, Proc. Workshop on 3D Digital Imaging and Modeling (3DIM), 2009 details
M. Ovsjanikov, A. M. Bronstein, M. M. Bronstein, L. Guibas, ShapeGoogle: a computer vision approach for invariant shape retrieval, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009 details
Y. Devir, G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, On reconstruction of non-rigid shapes with intrinsic regularization, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Topology-invariant similarity of nonrigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 81(3), 2009 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Partial similarity of objects, or how to compare a centaur to a horse, Int'l Journal of Computer Vision (IJCV), Vol. 84(2), 2009 details
A. M. Bronstein, M. M. Bronstein, Y. Carmon, R. Kimmel, Partial similarity of shapes using a statistical significance measure, IPSJ Trans. Computer Vision and Application, Vol. 1, 2009 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant geodesic geometry of deformable 3D shapes, arXiv:1012.5936, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant diffusion geometry for the analysis of deformable 3D shapes, arXiv:1012.5933, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Full and partial symmetries of non-rigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 89(1), 2010 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, M. Mahmoudi, G. Sapiro, A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching, Int'l Journal of Computer Vision (IJCV), Vol. 89(2), 2010 details
N. Mitra, A. M. Bronstein, M. M. Bronstein, Intrinsic regularity detection in 3D geometry, Proc. European Conf. Computer Vision (ECCV), 2010 details
A. M. Bronstein, M. M. Bronstein, Spatially-sensitive affine-invariant image descriptors, Proc. European Conf. Computer Vision (ECCV), 2010 details
M. M. Bronstein, A. M. Bronstein, F. Michel, N. Paragios, Data fusion through cross-modality metric learning using similarity-sensitive hashing, Proc. Computer Vision and Pattern Recognition (CVPR), 2010 details
D. Raviv, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Volumetric heat kernel signatures, Proc. Int'l Workshop on 3D Object Retrieval (3DOR), ACM Multimedia, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, G. Sapiro, Diffusion symmetries of non-rigid shapes, Proc. Int'l Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2010 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Nonlinear dimensionality reduction by topologically constrained isometric embedding, Intl. Journal of Computer Vision (IJCV), Vol. 89(1), 2010 details
A. M. Bronstein, M. M. Bronstein, U. Castellani, B. Falcidieno, A. Fusiello, A. Godil, L. J. Guibas, I. Kokkinos, Z. Lian, M. Ovsjanikov, G. Patané, M. Spagnuolo, R. Toldo, SHREC 2010: robust large-scale shape retrieval benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
A. M. Bronstein, M. M. Bronstein, B. Bustos, U. Castellani, M. Crisani, B. Falcidieno, L. J. Guibas, I. Kokkinos, V. Murino, M. Ovsjanikov, G. Patané, I. Sipiran, M. Spagnuolo, J. Sun, SHREC 2010: robust feature detection and description benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
A. M. Bronstein, M. M. Bronstein, U. Castellani, A. Dubrovina, L. J. Guibas, R. P. Horaud, R. Kimmel, D. Knossow, E. von Lavante, D. Mateus, M. Ovsjanikov, A. Sharma, SHREC 2010: robust correspondence benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
R. Kimmel, C. Zhang, A. M. Bronstein, M. M. Bronstein, Are MSER features really interesting?, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 33(11), 2011 details
A. M. Bronstein, Spectral descriptors for deformable shapes, arXiv:1110.5015, 2011 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant diffusion geometry for the analysis of deformable 3D shapes, Proc. Computer Vision and Pattern Recognition (CVPR), 2011 details
M. M. Bronstein, A. M. Bronstein, Shape recognition with spectral distances, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 33(5), 2011 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, A correspondence-less approach to matching of deformable shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Photometric heat kernel signatures, Proc. Scale Space and Variational Methods (SSVM), 2011 details
J. Aflalo, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Deformable shape retrieval by learning diffusion kernels, Proc. Scale Space and Variational Methods (SSVM), 2011 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, A. Wolf, R. Kimmel, Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
C. Wang, M. M. Bronstein, A. M. Bronstein, N. Paragios, Discrete minimum distortion correspondence problems for non-rigid shape matching, Proc. Scale Space and Variational Methods (SSVM), 2011 details
A. Hooda, M. M. Bronstein, A. M. Bronstein, R. Horaud, Shape palindromes: analysis of intrinsic symmetries in 2D articulated shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
F. Michel, M. M. Bronstein, A. M. Bronstein, N. Paragios, Boosted metric learning for 3D multi-modal deformable registration, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2011 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant geodesic geometry of deformable 3D shapes, Computers and Graphics (CAG), Vol. 35(3), 2011 details
R. Litman, A. M. Bronstein, A. M. Bronstein, Diffusion-geometric maximally stable component detection in deformable shapes, Computers and Graphics (CAG), Vol. 35(3), 2011 details
A. M. Bronstein, M. M. Bronstein, M. Ovsjanikov, L. J. Guibas, Shape Google: geometric words and expressions for invariant shape retrieval, ACM Trans. Graphics (TOG), Vol. 30(1), 2011 details
A. M. Bronstein, M. M. Bronstein, Metric approaches to invariant shape similarity, Chapter in Handbook of Mathematical Methods in Imaging (O. Scherzer Ed.), Springer, 2011 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Real-time online singing voice separation from monaural recordings using robust low-rank modeling, Proc. Annual Conference of the Int'l Society for Music Information Retrieval (ISMIR), 2012 (Best poster presentation award) details
O. Litany, A. M. Bronstein, M. M. Bronstein, Putting the pieces together: regularized multi-shape partial matching, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
A. Kovnatsky, A. M. Bronstein, M. M. Bronstein, Stable spectral mesh filtering, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
I. Kokkinos, M. M. Bronstein, R. Litman, A. M. Bronstein, Intrinsic shape context descriptors for deformable shapes, Proc. Computer Vision and Pattern Recognition (CVPR), 2012 details
E. Rodolà, A. M. Bronstein, A. Albarelli, F. Bergamasco, A. Torsello, A game-theoretic approach to deformable shape matching, Proc. Computer Vision and Pattern Recognition (CVPR), 2012 details
M. Spagnuolo, M. M. Bronstein, A. M. Bronstein, A. Ferreira (Eds.), Eurographics Workshop on 3D Object Retrieval, Eurographics Association, 2012, ISBN: 978-3-905674-36-1 details
R. Litman, A. M. Bronstein, M. M. Bronstein, Stable volumetric features in deformable shapes, Computers and Graphics (CAG), Vol. 36(5), 2012 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, X.-C. Tai, R. Kimmel, Group-valued regularization for analysis of articulated motion, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Learning efficient structured sparse models, Proc. Int'l Conf. on Machine Learning (ICML), 2012 details
A. Zabatani, A. M. Bronstein, Parallelized algorithms for rigid surface alignment on GPU, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Articulated motion segmentation of point clouds by group-valued regularization, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, D. Raviv, R. Kimmel, Affine-invariant photometric heat kernel signatures, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
C. Strecha, A. M. Bronstein, M. M. Bronstein, P. Fua, LDAHash: improved matching with smaller descriptors, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 34(1), 2012 details
B. M. Bruckstein, B. ter haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.), Scale Space and Variational Methods in Computer Vision, Lecture Notes in Computer Science (LNCS) No. 6667, Springer, 2012, ISBN: 978-3-642-24784-2 details
R. Litman, A. M. Bronstein, M. M. Bronstein, Stable semi-local features for non-rigid shapes, Chapter in Innovations for Shape Analysis: Models and Algorithms (M. Breuss, A. M. Bruckstein, P. Maragos Eds.), Springer, 2012 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, A. Wolf, R. Kimmel, Group-valued regularization for motion segmentation of articulated shapes, Chapter in Innovations for Shape Analysis: Models and Algorithms (M. Breuss, A. M. Bruckstein, P. Maragos Eds.), Springer, 2012 details
A. M. Bronstein, M. M. Bronstein, M. Ovsjanikov, 3D features, surface descriptors, and object descriptors, Chapter in 3D Imaging, Analysis and Applications (N. Pears, Y. Liu, P. Bunting, Eds.), Springer, 2012. details
P. Sprechmann, R. Litman, T. Ben Yakar, A. M. Bronstein, G. Sapiro, Efficient supervised sparse analysis and synthesis operators, Proc. Neural Information Proc. Systems (NIPS), 2013 details
T. Ben Yakar, R. Litman, P. Sprechmann, A. M. Bronstein, G. Sapiro, Bilevel sparse models for polyphonic music transcription, Proc. Annual Conf. of the Int'l Society for Music Info. Retrieval (ISMIR), 2013 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro, Sparse modeling of intrinsic correspondences, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, K. Glashoff, R. Kimmel, Coupled quasi-harmonic bases, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details
P. Sprechmann, A. M. Bronstein, J.-M. Morel, G. Sapiro, Audio restoration from multiple copies, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details
P. Sprechmann, A. M. Bronstein, M. M. Bronstein, G. Sapiro, Learnable low rank sparse models for speech denoising, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details
A. Kovnatski, D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Geometric and photometric data fusion in non-rigid shape analysis, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, Partial shape matching without point-wise correspondence, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details
R. Litman, and A. M. Bronstein, Learning spectral descriptors for deformable shape correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(1), 2013 details
Q. Qiu, G. Sapiro, A. M. Bronstein, Random forests can hash, arXiv:1412.5083, 2014 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Supervised non-Euclidean sparse NMF via bilevel optimization with applications to speech enhancement, Proc. Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014 details
S. Korman, R. Litman, S. Avidan, A. M. Bronstein, Probably approximately symmetric: Fast rigid symmetry detection with global guarantees, Computer Graphics Forum (CGF), Vol. 34(1), 2014 details
R. Litman, A. M. Bronstein, M. M. Bronstein, U. Castellani, Supervised learning of bag-of-features shape descriptors using sparse coding, Computer Graphics Forum (CGF), Vol. 33(5), 2014 details
O. Menashe, A. M. Bronstein, Real-time compressed imaging of scattering volumes, Proc. Int'l Conf. on Image Processing (ICIP), 2014 details
S. Biasotti, A. Cerri, A. M. Bronstein, M. M. Bronstein, Quantifying 3D shape similarity using maps: Recent trends, applications and perspectives, Proc. EUROGRAPHICS STARS, 2014 details
J. Masci, M. M. Bronstein, A. M. Bronstein, J. Schmidhuber, Multimodal similarity-preserving hashing, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(4), 2014 details
J. Masci, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro, Sparse similarity-preserving hashing, Proc. Int'l Conf. on Learning Representations (ICLR), 2014 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Equi-affine invariant intrinsic geometries for bendable shapes analysis, Journal of Mathematical Imaging and Vision (JMIV), Vol. 50(1), 2014 details
D. Pickup, X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono, A. Ben Hamza, A. M. Bronstein, M. M. Bronstein, S. Bu, U. Castellani, S. Cheng, V. Garro, A. Giachetti, A. Godil, J. Han, H. Johan, L. Lai, B. Li, C. Li, H. Li, R. Litman, X. Liu, Z. Liu, Y. Lu, A. Tatsuma, J. Ye, Shape retrieval of non-rigid 3D human models, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2014 details
O. Litany, T. Remez, A. M. Bronstein, Image reconstruction from dense binary pixels, arXiv:1512.01774, 2015
D. Eynard, A. Kovnatsky, M. M. Bronstein, K. Glashoff, A. M. Bronstein, Multimodal manifold analysis using simultaneous diagonalization of Laplacians, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37(12), 2015 details
T. Remez, O. Litany, A. M. Bronstein, A Picture is Worth a Billion Bits: Real-time image reconstruction from dense binary pixels, arXiv:1510.04601, 2015 details
A. M. Bronstein, New dimensions of media, Universidad La Salle, Revista de ciencias de la computación, Vol. 3(1), 2015
H. Haim, A. M. Bronstein, E. Marom, Computational all-in-focus imaging using an optical phase mask, OSA Optics Express, Vol. 23(19), 2015 details
R. Litman, S. Korman, A. M. Bronstein, S. Avidan, GMD: Global model detection via inlier rate estimation, Proc. Computer Vision and Pattern Recognition (CVPR), 2015 details
I. Sipiran, B. Bustos, T. Schreck, A. M. Bronstein, M. M. Bronstein, U. Castellani, S. Choi, L. Lai, H. Li, R. Litman, L. Sun, SHREC'15 Track: Scalability of non-rigid 3D shape retrieval, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2015 details
X. Bian, H. Krim, A. M. Bronstein, L. Dai, Sparse null space basis pursuit and analysis dictionary learning for high-dimensional data analysis, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2015 details
Y. Aflalo, A. M. Bronstein, R. Kimmel, On convex relaxation of graph isomorphism, Proc. US National Academy of Sciences (PNAS), 2015 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Learning efficient sparse and low-rank models, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37(9), 2015 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Supervised non-negative matrix factorization for audio source separation, Chapter in Excursions in Harmonic Analysis (R. Balan, M. Begue, J. J. Benedetto, W. Czaja, K. Okoudjou Eds.), Birkhaeuser, 2015 details
Y. Choukroun, A. Shtern, A. M. Bronstein, R. Kimmel, Hamiltonian operator for spectral shape analysis, arXiv:1611.01990, 2016 details
A. M. Bronstein, Y. Choukroun, R. Kimmel, M. Sela, Consistent discretization and minimization of the L1 norm on manifolds, Proc. 3D Vision (3DV), 2016 details
R. Litman, A. M. Bronstein, SpectroMeter: Amortized sublinear spectral approximation of distance on graphs, Proc. 3D Vision (3DV), 2016 details
T. Remez, O. Litany, S. Yoseff, H. Haim, A. M. Bronstein, FPGA system for real-time computational extended depth of field imaging using phase aperture coding, arXiv:1608.01074, 2016 details
R. Giryes, G. Sapiro, A. M. Bronstein, Deep neural networks with random Gaussian weights: A universal classification strategy?, IEEE Trans. Signal Processing, Vol. 64(13), 2016 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, D. Cremers, Non-rigid puzzles, Computer Graphics Forum, Vol. 35(5), 2016 (SGP Best Paper Award) details
X. Bian, H. Krim, A. M. Bronstein, L. Dai, Sparsity and nullity: paradigms for analysis dictionary learning, SIAM J. Imaging Sci., Vol. 9(3), 2016 details
D. Pickup, X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono, A. Ben Hamza, A. M. Bronstein, M. M. Bronstein, S. Bu, U. Castellani, S. Cheng, V. Garro, A. Giachetti, A. Godil, J. Han, H. Johan, L. Lai, B. Li, C. Li, H. Li, R. Litman, X. Liu, Z. Liu, Y. Lu, A. Tatsuma, J. Ye, Shape retrieval of non-rigid 3D human models, Intl. Journal of Computer Vision (IJCV), 2016 details
S. Vedula, O. Senouf, A. M. Bronstein, O. V. Michailovich, M. Zibulevsky, Towards CT-quality ultrasound imaging using deep learning, arXiv:1710.06304, 2017 details
O. Litany, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, Deep Functional Maps: Structured prediction for dense shape correspondence, Proc. Int'l Conf. on Computer Vision (ICCV), 2017 details
Z. Laehner, M. Vestner, A. Boyarski, O. Litany, R. Slossberg, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, R. Kimmel, D. Cremers, Efficient deformable shape correspondence via kernel matching, Proc. 3D Vision (3DV), 2017 details
G. Alexandroni, Y. Podolsky, H. Greenspan, T. Remez, O. Litany, A. M. Bronstein, R. Giryes, White matter fiber representation using continuous dictionary learning, Proc. Int'l Conf. Medical Image Computing & Computer Assisted Intervention (MICCAI), 2017 details
M. Vestner, R. Litman, E. Rodolà, A. M. Bronstein, D. Cremers, Product Manifold Filter: Non-rigid shape correspondence via kernel density estimation in the product space, Proc. Computer Vision and Pattern Recognition (CVPR), 2017 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, Fully spectral partial shape matching, Computer Graphics Forum, Vol. 36(2), 2017 details
A. Boyarski, A. M. Bronstein, M. M. Bronstein, Subspace least squares multidimensional scaling, Proc. Scale Space and Variational Methods (SSVM), 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep class-aware image denoising, Proc. Int'l Conf. on Image Processing (ICIP), 2017 details
O. Litany, T. Remez, A. M. Bronstein, Cloud Dictionary: Sparse coding and modeling for point clouds, arXiv:1612.04956, 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep class-aware denoising, arXiv:1701.01698, 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep convolutional denoising of low-light images, arXiv:1701.01687, 2017 details
O. Litany, T. Remez, D. Freedman, L. Shapira, A. M. Bronstein, R. Gal, ASIST: Automatic Semantically Invariant Scene Transformation, Computer Vision and Image Understanding, Vol. 157, 2017 details
M. Ovsjanikov, E. Corman, M. M. Bronstein, E. Rodolà, M. Ben-Chen, L. Guibas, F. Chazal, A. M. Bronstein, Computing and processing correspondences with functional maps, SIGGRAPH Courses, 2017 details
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, R. Feris, A. Kumar, R. Giryes, A. M. Bronstein, ∆-encoder: an effective sample synthesis method for few-shot object recognition, Proc. Neural Information Processing Systems (NIPS), 2018 details
E. Rodolà, Z. Lähner, A. M. Bronstein, M. M. Bronstein, J. Solomon, Functional maps representation on product manifolds, arXiv:1809.10940, 2018 details
C. Baskin, N. Liss, Y. Chai, E. Zheltonozhskii, E. Schwartz, R. Giryes, A. Mendelson, A. M. Bronstein, NICE: noise injection and clamping estimation for neural network quantization, arXiv:1810.00162, 2018 details
Q. Qiu, J. Lezama, A. M. Bronstein, G. Sapiro, ForestHash: Semantic hashing with shallow random forests and tiny convolutional networks, Proc. European Conf. on Computer Vision (ECCV), 2018 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Class-aware fully-convolutional Gaussian and Poisson denoising, IEEE Trans. Image Processing, Vol. 27(11), 2018 details
A. Tsitsulin, D. Mottin, P. Karras, A. M. Bronstein, E, Mueller, NetLSD: Hearing the shape of a graph, Proc. ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2018 details
O. Senouf, S. Vedula, G. Zurakhov, A. M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Blondheim, High frame-rate cardiac ultrasound imaging with deep learning, Proc. Int'l Conf. Medical Image Computing & Computer Assisted Intervention (MICCAI), 2018 details
S. Vedula, O. Senouf, G. Zurakhov, A. M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Gaitini, High quality ultrasonic multi-line transmission through deep learning, Proc. Machine Learning for Medical Image Reconstruction (MLMIR), 2018 details
A. Tsitsulin, D. Mottin, P. Karras, A. M. Bronstein, E, Mueller, SGR: Self-supervised spectral graph representation learning, Proc. KDD Deep Learning Day, 2018 details
E. Schwartz, R. Giryes, A. M. Bronstein, DeepISP: Towards learning an end-to-end image processing pipeline, IEEE Trans. on Image Processing, 2018 details
H. Haim, S. Elmalem, R. Giryes, A. M. Bronstein, E. Marom, Depth estimation from a single image using deep learned phase coded mask, IEEE Trans. Computational Imaging, Vol. 2(3), 2018 (Winner of the OSA Student Grand Challenge The Optical System of the Future) details
E. Tsitsin, A. M. Bronstein, T. Hendler, M. Medvedovsky, Passive electric impedance tomography, Proc. Electric Impedance Tomography (EIT), 2018 details
E. Tsitsin, T. Mund, A. M. Bronstein, Printable anisotropic phantom for EEG with distributed current sources, Proc. IEEE Int'l Symposium on Biomedical Imaging (ISBI), 2018 details
E. Tsitsin, M. Medvedovsky, A. M. Bronstein, VibroEEG: Improved EEG source reconstruction by combined acoustic-electric imaging, Proc. IEEE Int'l Symposium on Biomedical Imaging (ISBI), 2018 details
C. Baskin, N. Liss, E. Zheltonozhskii, A. M. Bronstein, A. Mendelson, Streaming architectures for large-scale quantized neural networks on an FPGA-based dataflow platform, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 details
R. Giryes, Y. C. Eldar, A. M. Bronstein, G. Sapiro, Tradeoffs between convergence speed and reconstruction accuracy in inverse problems, IEEE Trans. on Signal Processing, Vol. 66(7), 2018 details
A. Boyarski, S. Vedula, A. M. Bronstein, Deep matrix factorization with spectral geometric regularization, arXiv: 1911.07255, 2019 details
Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, Loss aware post-training quantization, arXiv: 1911.07190, 2019 details
Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A. M. Bronstein, A. Mendelson, Smoothed inference for adversarially-trained models, arXiv: 1911.07198, 2019 details
S. Doveh, E. Schwartz, C. Xue, R. Feris, A. M. Bronstein, R. Giryes, L. Karlinsky, MetAdapt: Meta-learned task-adaptive architecture for few-shot classification, arXiv: 1912.00412, 2019 details
E. Rozenberg, D. Freedman, A. M. Bronstein, Localization with limited annotation for chest X-rays, Proc. ML4H, NeurIPS, 2019 details
S. Vedula, O. Senouf, G. Zurakov, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Learning beamforming in ultrasound imaging, Proc. Medical Imaging with Deep Learning (MIDL), 2019 details
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, R. Feris, A. Kumar, R. Giryes, A. M. Bronstein, RepMet: Representative-based metric learning for classification and one-shot object detection, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
O. Halimi, O. Litany, E. Rodolà, A. M. Bronstein, R. Kimmel, Self-supervised learning of dense shape correspondence, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, A. M. Bronstein, LaSO: Label-Set Operations networks for multi-label few-shot learning, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
A. Zabatani, V. Surazhsky, E. Sperling, S. Ben Moshe, O. Menashe, D. H. Silver, Z. Karni, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Intel RealSense SR300 Coded light depth Camera, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2019 details
Y. Zur, C. Baskin, E. Zheltonozhskii, B. Chmiel, I. Evron, A. M. Bronstein, A. Mendelson, Towards learning of filter-level heterogeneous compression of convolutional neural networks, Proc. AutoML Workshop, Int'l Conf. on Machine Learning (ICML), 2019 details
E. Schwartz, L. Karlinsky, R. Feris, R. Giryes, A. M. Bronstein, Baby steps towards few-shot learning with multiple semantics, arXiv:1906.01905, 2019 details
A. Rampini, I. Tallini, M. Ovsjanikov, A. M. Bronstein, E. Rodola, Correspondence-free region localization for partial shape similarity via Hamiltonian spectrum alignment, Proc. 3D Vision (3DV), 2019 (Best paper award) details
O. Senouf, S. Vedula, T. Weiss, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Self-supervised learning of inverse problem solvers in medical imaging, Proc. Medical Image Learning with Less Labels and Imperfect Data, MICCAI 2019 details
R. M. Dyke, C Stride, Y.-K. Lai, P. L. Rosin, M. Aubry, A. Boyarski, A. M. Bronstein, M. M. Bronstein, D. Cremers, M. Fisher, T. Groueix, D. Guo, V. G. Kim, R. Kimmel, Z. Lähner, K. Li, O. Litany, T. Remez, E. Rodolà, B. C. Russell, Y. Sahillioglu, R. Slossberg, M. Vestner, Z. Wu, J. Yang, Gary Tam, Shape Correspondence with Isometric and Non-Isometric Deformations, Eurographics Workshop on 3D Object Retrieval, 2019 details
N. Diamant, D. Zadok, C. Baskin, E. Schwartz, A. M. Bronstein, Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level, Proc. Int'l Conf. on Image Processing (ICIP), 2019 details
G. Pai, R. Talmon, A. M. Bronstein, R. Kimmel, DIMAL: Deep isometric manifold learning using sparse geodesic sampling, Proc. IEEE Winter Conf. on Applications of Computer Vision (WACV), 2019 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, D. Cremers, Partial single- and multi-shape dense correspondence using functional maps, Chapter in The Handbook of Numerical Analysis - Processing, Analyzing and Learning of Images, Shapes, and Forms, Elsevier, 2019 details
A. Boyarski, A. M. Bronstein, Multidimensional scaling, Computer Vision: A Reference Guide, (Katsushi Ikeuchi, Ed.) details
C. Baskin, E. Schwartz, E. Zheltonozhskii, N. Liss, R. Giryes, A. M. Bronstein, A. Mendelson, UNIQ: Uniform noise injection for non-uniform quantization of neural networks, ACM Transactions on Computer Systems (TOCS), 2020 details
B. Finkelshtein, C. Baskin, E. Zheltonozhskii, U. Alon, Single-node attack for fooling graph neural networks, arXiv:2011.03574, 2020 details
J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, A. M. Bronstein, 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI, Proc. Machine Learning for Medical Image Reconstruction, MICCAI 2020 details
T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A. M. Bronstein, Towards learned optimal q-space sampling in diffusion MRI, Proc. Computational Diffusion MRI, MICCAI 2020 details
E. Zheltonozhskii, C. Baskin, A. M. Bronstein, A. Mendelson, Self-supervised learning for large-scale unsupervised image clustering, NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice, 2020 details
G. Mariani, L. Cosmo, A. M. Bronstein, E. Rodolà, Generating adversarial surfaces via band-limited perturbations, Computer Graphics Forum, 2020 details
B. Chmiel, C. Baskin, R. Banner, E. Zheltonozshkii, Y. Yermolin, A. Karbachevsky, A. M. Bronstein, A. Mendelson, Feature map transform coding for energy-efficient CNN inference, Proc. Intl. Joint Conf. on Neural Networks (IJCNN), 2020 details
E. Amrani, R. Ben-Ari, T. Hakim, A. M. Bronstein, Self-Supervised Object Detection and Retrieval Using Unlabeled Videos, CVPR workshop, 2020 details
D. H. Silver, M. Feder, Y. Gold-Zamir, A. L. Polsky, S. Rosentraub, E. Shachor, A. Weinberger, P. Mazur, V. D. Zukin, A. M. Bronstein, Data-driven prediction of embryo implantation probability using IVF time-lapse imaging, Proc. MIDL, 2020 details
T. Weiss, S. Vedula, O. Senouf, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Joint learning of Cartesian undersampling and reconstruction for accelerated MRI, Proc. Int’l Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2020 details
S. Sommer, A. M. Bronstein, Horizontal flows and manifold stochastics in geometric deep learning, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2020 details
K. Rotker, D. Ben-Bashat, A. M. Bronstein, Over-parameterized models for vector fields, SIAM Journal on Imaging Sciences (SIIMS), 2020 details
A. Tsitsulin, M. Munkhoeva, D. Mottin, P. Karras. A. M. Bronstein, I. Oseledets, E. Müller, Intrinsic multi-scale evaluation of generative models, Proc. ICLR, 2020 details
A. Karbachevsky, C. Baskin, E. Zheltonozshkii, Y. Yermolin, F. Gabbay, A. M. Bronstein, A. Mendelson, HCM: Hardware-aware complexity metric for neural network architectures, arXiv:2004.08906, 2020 details
E. Zheltonozhskii, C. Baskin, Y. Nemcovsky, B. Chmiel, A. Mendelson, A. M. Bronstein, Colored noise injection for training adversarially robust neural networks, arXiv:2003.02188, 2020 details
A. Livne, A. M. Bronstein, R. Kimmel, Z. Aviv, S. Grofit, Do we need depth in state-of-the-art face authentication?, Proc. IEEE Int'l Conf. on 3D Vision (3DV), 2020 details
M. Shkolnik, B. Chmiel, R. Banner, G. Shomron, Y. Nahshan, A. M. Bronstein, U. Weiser, Robust Quantization: One Model to Rule Them All, Proc. NeurIPS, 2020 details
Y. Choukroun , A. Shtern, A. M. Bronstein, R. Kimmel, Hamiltonian operator for spectral shape analysis, IEEE Trans. Vis. and Comp. Graphics, vol. 26(2), 2020 details
A. Arbelle, S. Doveh, A. Alfassy, J. Shtok, G. Lev, E. Schwartz, H. Kuehne, H. Barak Levi, P. Sattigeri, R. Panda, C.-F. Chen, A. M. Bronstein, K. Saenko, S. Ullman, R. Giryes, R. Feris, L. Karlinsky, Detector-free weakly supervised grounding by separation, Proc. CVPR, 2022 details
S. Doveh, E. Schwartz, C. Xue, R. Feris, A. M. Bronstein, R. Giryes, L. Karlinsky, MetAdapt: Meta-learned task-adaptive architecture for few-shot classification, Pattern Recognition Letters, 2021 details
T. Weiss, N. Peretz, S. Vedula, A. Feuer, A. M. Bronstein, Joint optimization of system design and reconstruction in MIMO radar imaging, Proc. IEEE Int'l Workshop on Machine Learning for Signal Processing, 2021 details
Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, Loss aware post-training quantization, Machine Learning, 2021 details
C. Baskin, B. Chmiel, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, CAT: Compression-aware training for bandwidth reduction, JMLR, 2021 details
E. Rozenberg, D. Freedman, A. M. Bronstein, Learning to localize objects using limited annotation with applications to thoracic diseases, IEEE Access Vol. 9, 2021 details
T. Weiss, O. Senouf, S. Vedula, O. Michailovich, M. Zibulevsky, A. M. Bronstein, PILOT: Physics-Informed Learned Optimal Trajectories for accelerated MRI, Journal of Machine Learning for Biomedical Imaging (MELBA), 2021 details
Y. Elul, A. Rosenberg, A. Schuster, A. M. Bronstein, Y. Yaniv, Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis, Proc. US National Academy of Sciences (PNAS), 2021 details
L. Karlinsky, J. Shtok, A. Alfassy, M. Lichtenstein, S. Harary, E. Schwartz, S. Doveh, P. Sattigeri, R. Feris, A. M. Bronstein, R. Giryes, StarNet: towards weakly supervised few-shot detection and explainable few-shot classification, Proc. AAAI, 2021 details
E. Amrani, R. Ben-Ari, D. Rotman, A. M. Bronstein, Noise estimation using density estimation for self-supervised multimodal learning, Proc. AAAI, 2021 details
O. Dahary, M. Jacoby, A. M. Bronstein, Digital Gimbal: End-to-end deep image stabilization with learnable exposure times, Proc. CVPR, 2021 details
A. Boyarski, S. Vedula, A. M. Bronstein, Spectral geometric matrix completion, Proc. Mathematical and Scientific Machine Learning, 2021 details
E. Rozenberg, A. Karnieli, O. Yesharim, S. Trajtenberg-Mills, D. Freedman, A. M. Bronstein, A. Arie, Inverse design of quantum holograms in three-dimensional nonlinear photonic crystals, CLEO, 2021 details
A. Karbachevsky, C. Baskin, E. Zheltonozshkii, Y. Yermolin, F. Gabbay, A. M. Bronstein, A. Mendelson, Early-stage neural network hardware performance analysis, Sustainability 13(2):717, 2021 details
J. Hermanns, A. Tsitsulin, M. Munkhoeva, A. M. Bronstein, D. Mottin, P. Karras, GRASP: Graph Alignment through Spectral Signatures, Proc. Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, 2022 details
P. Kang, Z. Lin, Z. Yang, A. M. Bronstein, Q. Li, W. Liu, Deep fused two-step cross-modal hashing with multiple semantic supervision, Multimedia Tools and Applications, 2022 details
P. Kang, Z. Lin, Z. Yang, X. Fang, A. M. Bronstein, Q. Li, W. Liu, Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval, Applied Intelligence, 52(1), pp. 33-54, 2022 details
Y. Nemcovsky, M. Jacoby, A. M. Bronstein, C. Baskin, Physical passive patch adversarial attacks on visual odometry systems, Proc. ACCV, 2022 details
L. Ackerman-Schraier, A. A. Rosenberg, A. Marx, A. M. Bronstein, Machine learning approaches demonstrate that protein structures carry information about their genetic coding, Nature Scientific Reports, 2022 details
A. A. Rosenberg, N. Yehishalom, A. Marx, A. M. Bronstein, Defining amino acid pairs as structural units suggests mutation sensitivity to adjacent residues, biorXiv/2022/513383, 2022 details
A. Rosenberg, A. Marx, A. M. Bronstein, Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon, Nature Communications, 2022 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, D. Freedman, A. M. Bronstein, A. Arie, Inverse design of spontaneous parametric downconversion for generation of high-dimensional qudits, Optica 9, 602-615, 2022 details
N. Talati, H. Ye, S. Vedula, K.-Y. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw, A. M. Bronstein, T. Mudge, R. Dreslinski, Mint: An Accelerator For Mining Temporal Motifs, Proc. MICRO, 2022 details
E. Zheltonozhskii, C. Baskin, A. Mendelson, A. M. Bronstein, O. Litany, Contrast to divide: Self-supervised pre-training for learning with noisy labels, Proc. of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022 details
G. Pai, A. Bronstein, R. Talmon, R. Kimmel, Deep isometric maps, Image and Vision Computing, 2022 details
N. Diamant, N. Shandor, A. M. Bronstein, Delta-GAN-Encoder: Encoding semantic changes for explicit image editing, using few synthetic samples, arXiv:2111.08419, 2022 details
T. Blau, R. Ganz, B. Kawar, A. M. Bronstein, M. Elad , Threat model-agnostic adversarial defense using diffusion models, arXiv preprint arXiv:2207.08089, 2022 details
D. E. Fordham, D. Rosentraub, A. L. Polsky, T. Aviram, Y. Wolf, O. Perl, A. Devir, S. Rosentraub, D. H. Silver, Y. Gold Zamir, A. M. Bronstein, M. Lara Lara, J. Ben Nagi, A. Alvarez, S. Munné, Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?, Human Reproduction, Volume 37, Issue 10, Pages 2275–2290, 2022 details
E. Amrani, A. M. Bronstein, Self-supervised classification network, Proc. ECCV, 2022 details
A. A. Rosenberg, N. Yehishalom, A. Marx, A. M. Bronstein, An amino-domino model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural units, Proc. US National Academy of Sciences (PNAS), 2023 details
T. Weiss, L. Cosmo, E. Mayo Yanes, S. Chakraborty, A. M. Bronstein, R. Gershoni-Poranne, Guided diffusion for inverse molecular design, Nature Computational Science 3(10), 873–882, 2023 details
E. Schwartz, A. M. Bronstein, R. Giryes, ISP distillation, IEEE Open Journal of Signal Processing 4, 12-20, 2023 details
T. Blau, R. Ganz, C. Baskin, M. Elad, A. M. Bronstein, Classifier robustness enhancement via test-time transformation, arXiv preprint arXiv:2303.15409 2023 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, S. Mishra, S. Prabhakar, R. P. Singh, D. Freedman, A. M. Bronstein, A. Arie, Designing nonlinear photonic crystals for high-dimensional quantum state engineering, ICLR Workshop on Machine Learning for Materials, 2023 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, S. Mishra, S. Prabhakar, R. P. Singh, D. Freedman, A. M. Bronstein, A. Arie, A machine learning approach to generate quantum light, ICLR Workshop on Physics for Machine Learning, 2023 details
H. Ye, S. Vedula, Y. Chen, Y. Yang, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference, Proc. ACM Int'l Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023 details
S. Vedula, I. Tallini, A. A. Rosenberg, M. Pegoraro, E. Rodolà, Y. Romano, A. M. Bronstein, Continuous vector quantile regression, Proc. ICML Workshop Frontiers4LCD, 2023 details
M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein, Vector quantile regression on manifolds, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 details
T. Weiss, A. Wahab, A. M. Bronstein, R. Gershoni-Poranne, Interpretable deep learning unveils structure-property relationships in polybenzenoid hydrocarbons, Journal of Organic Chemistry, 2023 details
A. A. Rosenberg, S. Vedula, Y. Romano, A. M. Bronstein, Fast nonlinear vector quantile regression, Proc. ICML, 2023 details
D. Zadok, O. Salzman, A. Wolf, A. M. Bronstein, Towards predicting fine finger motions from ultrasound images via kinematic representation, Proc. ICRA, 2023 details
A. M. Bronstein, A. Marx, Water stabilizes an alternate turn conformation in horse heart myoglobin, Nature Scientific Reports, 2023 details
B. Gahtan, R. Cohen, A. M. Bronstein, G. Kedar, Using deep reinforcement learning for mmWave real-time scheduling, Proc. Int'l Conf. Network of the Future (NoF), 2023 details
T. Shor, T. Weiss, D. Noti, A. M. Bronstein, Multi PILOT: Feasible learned multiple acquisition trajectories for dynamic MRI, Proc. Medical Imaging with Deep Learning (MIDL), 2023 details
A. B. Bainson, J. Hermanns, P. Petsinis, N. Aavad, C. Dam Larsen, T. Swayne, A. Boyarski, D. Mottin, A. M. Bronstein, P. Karras, Spectral subgraph localization, Proc. Learning on Graphs Conference, 2023 details
S. A. Maddipatla, N. Bojan Sellam, S. Vedula, A. Marx, A. M. Bronstein, Generative modeling of protein ensembles guided by crystallographic electron densities, NeurIPS Workshop on Machine Learning in Structural Biology (MLSB), 2024 details
T. Blau, M. Kimhi, Y. Belinkov, A. M. Bronstein, C. Baskin, Context-aware prompt tuning: advancing in-context learning with adversarial methods, arXiv:2410.17222, 2024 details
B. Gahtan, R. J. Sahala, A. M. Bronstein, R. Cohen, Exploring QUIC dynamics: a large-scale dataset for encrypted traffic analysis, arXiv:2410.03728, 2024 details
B. Gahtan, S. Funk, E. Kodesh, I. Ketko, T. Kuflik, A. M. Bronstein, Automatic identification and visualization of group training activities using wearable data, arXiv:2410.05452, 2024 details
B. Gahtan, R. J. Shahla, R. Cohen, A. M. Bronstein, Estimating the number of HTTP/3 responses in QUIC using deep learning, arXiv:2410.06140, 2024 details
H. Abraham, B. Gahtan, A. Kobovich, O. Leitersdorf, A. M. Bronstein, E. Yaakobi, Beyond the alphabet: deep signal embedding for enhanced DNA clustering, arXiv:2410.06188, 2024 details
B. Gahtan, R. Cohen, A. M. Bronstein, E. Shapira, Data-driven cellular network selector for vehicle teleoperations, arXiv:2410.19791, 2024 details
T. Shor, C. Baskin, A. M. Bronstein, Leveraging latents for efficient thermography classification and segmentation, Proc. Medical Imaging with Deep Learning (MIDL), 2024 details
A. A. Rosenberg, S. Vedula, A. M. Bronstein, A. Marx, Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures, bioRxiv 2024.08.31.610605, 2024 details
S. Vedula, V. Maiorca, L. Basile, F. Locatello, A. M. Bronstein, Scalable unsupervised alignment of general metric and non-metric structures, arXiv preprint arXiv:2406.13507, 2024 (also in Proc. ICML Workshop on AI4Science) details
A. A. Rosenberg, A. Marx, A. M. Bronstein, A dataset of alternately located segments in protein crystal structures, Scientific Data, 11 (783), 2024 details
Y. Elul, E. Rozenberg, A. Boyarski, Y. Yaniv, A. Schuster, A. M. Bronstein , Data-driven modeling of interrelated dynamical systems, Nature Communications Physics (7), 144, 2024 details
O. Wengrowicz, A. M. Bronstein, O. Cohen, Unsupervised physics-informed deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography, Optics Express 32(6), pp. 8791-8803, 2024 details
G. Serussi, T. Shor, T. Hirshberg, C. Baskin, A. M. Bronstein, Active propulsion noise shaping for multi-rotor aircraft localization, Proc. Int'l Conf. on Intelligent Robots and Systems (IROS), 2024 details
M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein, Vector quantile regression on manifolds, Proc. AIStats, 2024 details
Y. Chen, H. Ye, S. Vedula, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, Demystifying graph sparsification algorithms in graph properties preservation, Proc.Int'l Conf. on Very Large Databases (VLDB), 2024 details
E. Amrani, L. Karlinsky, A. M. Bronstein, Sample- and parameter-efficient auto-regressive image models, Proc. CVPR, 2025 details
D. Freedman, E. Rozenberg, A. M. Bronstein, A theoretical framework for an efficient normalizing flow-based solution to the Schrödinger equation, Proc. AAAI, 2025 details
A. Maddipatla, N. Bojan Sellam, M. Bojan, S. Vedula, P. Schanda, A. Marx, A. M. Bronstein, Inverse problems with experiment-guided AlphaFold, arXiv:2502.09372, 2025 details
Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, R. Gershoni-Poranne, How local is "local"? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons, chemrxiv 10.26434/chemrxiv-2025-pqmcc, 2025 details
E. Amrani, L. Karlinsky, A. M. Bronstein, Sample- and parameter-efficient auto-regressive image models, Proc. CVPR, 2025 details
D. Freedman, E. Rozenberg, A. M. Bronstein, A theoretical framework for an efficient normalizing flow-based solution to the Schrödinger equation, Proc. AAAI, 2025 details
A. Maddipatla, N. Bojan Sellam, M. Bojan, S. Vedula, P. Schanda, A. Marx, A. M. Bronstein, Inverse problems with experiment-guided AlphaFold, arXiv:2502.09372, 2025 details
Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, R. Gershoni-Poranne, How local is "local"? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons, chemrxiv 10.26434/chemrxiv-2025-pqmcc, 2025 details
S. A. Maddipatla, N. Bojan Sellam, S. Vedula, A. Marx, A. M. Bronstein, Generative modeling of protein ensembles guided by crystallographic electron densities, NeurIPS Workshop on Machine Learning in Structural Biology (MLSB), 2024 details
T. Blau, M. Kimhi, Y. Belinkov, A. M. Bronstein, C. Baskin, Context-aware prompt tuning: advancing in-context learning with adversarial methods, arXiv:2410.17222, 2024 details
B. Gahtan, R. J. Sahala, A. M. Bronstein, R. Cohen, Exploring QUIC dynamics: a large-scale dataset for encrypted traffic analysis, arXiv:2410.03728, 2024 details
B. Gahtan, S. Funk, E. Kodesh, I. Ketko, T. Kuflik, A. M. Bronstein, Automatic identification and visualization of group training activities using wearable data, arXiv:2410.05452, 2024 details
B. Gahtan, R. J. Shahla, R. Cohen, A. M. Bronstein, Estimating the number of HTTP/3 responses in QUIC using deep learning, arXiv:2410.06140, 2024 details
H. Abraham, B. Gahtan, A. Kobovich, O. Leitersdorf, A. M. Bronstein, E. Yaakobi, Beyond the alphabet: deep signal embedding for enhanced DNA clustering, arXiv:2410.06188, 2024 details
B. Gahtan, R. Cohen, A. M. Bronstein, E. Shapira, Data-driven cellular network selector for vehicle teleoperations, arXiv:2410.19791, 2024 details
T. Shor, C. Baskin, A. M. Bronstein, Leveraging latents for efficient thermography classification and segmentation, Proc. Medical Imaging with Deep Learning (MIDL), 2024 details
A. A. Rosenberg, S. Vedula, A. M. Bronstein, A. Marx, Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures, bioRxiv 2024.08.31.610605, 2024 details
S. Vedula, V. Maiorca, L. Basile, F. Locatello, A. M. Bronstein, Scalable unsupervised alignment of general metric and non-metric structures, arXiv preprint arXiv:2406.13507, 2024 (also in Proc. ICML Workshop on AI4Science) details
A. A. Rosenberg, A. Marx, A. M. Bronstein, A dataset of alternately located segments in protein crystal structures, Scientific Data, 11 (783), 2024 details
Y. Elul, E. Rozenberg, A. Boyarski, Y. Yaniv, A. Schuster, A. M. Bronstein , Data-driven modeling of interrelated dynamical systems, Nature Communications Physics (7), 144, 2024 details
O. Wengrowicz, A. M. Bronstein, O. Cohen, Unsupervised physics-informed deep learning-based reconstruction for time-resolved imaging by multiplexed ptychography, Optics Express 32(6), pp. 8791-8803, 2024 details
G. Serussi, T. Shor, T. Hirshberg, C. Baskin, A. M. Bronstein, Active propulsion noise shaping for multi-rotor aircraft localization, Proc. Int'l Conf. on Intelligent Robots and Systems (IROS), 2024 details
M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein, Vector quantile regression on manifolds, Proc. AIStats, 2024 details
Y. Chen, H. Ye, S. Vedula, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, Demystifying graph sparsification algorithms in graph properties preservation, Proc.Int'l Conf. on Very Large Databases (VLDB), 2024 details
A. A. Rosenberg, N. Yehishalom, A. Marx, A. M. Bronstein, An amino-domino model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural units, Proc. US National Academy of Sciences (PNAS), 2023 details
T. Weiss, L. Cosmo, E. Mayo Yanes, S. Chakraborty, A. M. Bronstein, R. Gershoni-Poranne, Guided diffusion for inverse molecular design, Nature Computational Science 3(10), 873–882, 2023 details
E. Schwartz, A. M. Bronstein, R. Giryes, ISP distillation, IEEE Open Journal of Signal Processing 4, 12-20, 2023 details
T. Blau, R. Ganz, C. Baskin, M. Elad, A. M. Bronstein, Classifier robustness enhancement via test-time transformation, arXiv preprint arXiv:2303.15409 2023 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, S. Mishra, S. Prabhakar, R. P. Singh, D. Freedman, A. M. Bronstein, A. Arie, Designing nonlinear photonic crystals for high-dimensional quantum state engineering, ICLR Workshop on Machine Learning for Materials, 2023 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, S. Mishra, S. Prabhakar, R. P. Singh, D. Freedman, A. M. Bronstein, A. Arie, A machine learning approach to generate quantum light, ICLR Workshop on Physics for Machine Learning, 2023 details
H. Ye, S. Vedula, Y. Chen, Y. Yang, A. M. Bronstein, R. Dreslinski, T. Mudge, N. Talati, GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference, Proc. ACM Int'l Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023 details
S. Vedula, I. Tallini, A. A. Rosenberg, M. Pegoraro, E. Rodolà, Y. Romano, A. M. Bronstein, Continuous vector quantile regression, Proc. ICML Workshop Frontiers4LCD, 2023 details
M. Pegoraro, S. Vedula, A. A. Rosenberg, I. Tallini, E. Rodolà, A. M. Bronstein, Vector quantile regression on manifolds, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 details
T. Weiss, A. Wahab, A. M. Bronstein, R. Gershoni-Poranne, Interpretable deep learning unveils structure-property relationships in polybenzenoid hydrocarbons, Journal of Organic Chemistry, 2023 details
A. A. Rosenberg, S. Vedula, Y. Romano, A. M. Bronstein, Fast nonlinear vector quantile regression, Proc. ICML, 2023 details
D. Zadok, O. Salzman, A. Wolf, A. M. Bronstein, Towards predicting fine finger motions from ultrasound images via kinematic representation, Proc. ICRA, 2023 details
A. M. Bronstein, A. Marx, Water stabilizes an alternate turn conformation in horse heart myoglobin, Nature Scientific Reports, 2023 details
B. Gahtan, R. Cohen, A. M. Bronstein, G. Kedar, Using deep reinforcement learning for mmWave real-time scheduling, Proc. Int'l Conf. Network of the Future (NoF), 2023 details
T. Shor, T. Weiss, D. Noti, A. M. Bronstein, Multi PILOT: Feasible learned multiple acquisition trajectories for dynamic MRI, Proc. Medical Imaging with Deep Learning (MIDL), 2023 details
A. B. Bainson, J. Hermanns, P. Petsinis, N. Aavad, C. Dam Larsen, T. Swayne, A. Boyarski, D. Mottin, A. M. Bronstein, P. Karras, Spectral subgraph localization, Proc. Learning on Graphs Conference, 2023 details
J. Hermanns, A. Tsitsulin, M. Munkhoeva, A. M. Bronstein, D. Mottin, P. Karras, GRASP: Graph Alignment through Spectral Signatures, Proc. Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, 2022 details
P. Kang, Z. Lin, Z. Yang, A. M. Bronstein, Q. Li, W. Liu, Deep fused two-step cross-modal hashing with multiple semantic supervision, Multimedia Tools and Applications, 2022 details
P. Kang, Z. Lin, Z. Yang, X. Fang, A. M. Bronstein, Q. Li, W. Liu, Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval, Applied Intelligence, 52(1), pp. 33-54, 2022 details
Y. Nemcovsky, M. Jacoby, A. M. Bronstein, C. Baskin, Physical passive patch adversarial attacks on visual odometry systems, Proc. ACCV, 2022 details
L. Ackerman-Schraier, A. A. Rosenberg, A. Marx, A. M. Bronstein, Machine learning approaches demonstrate that protein structures carry information about their genetic coding, Nature Scientific Reports, 2022 details
A. A. Rosenberg, N. Yehishalom, A. Marx, A. M. Bronstein, Defining amino acid pairs as structural units suggests mutation sensitivity to adjacent residues, biorXiv/2022/513383, 2022 details
A. Rosenberg, A. Marx, A. M. Bronstein, Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon, Nature Communications, 2022 details
E. Rozenberg, A. Karnieli, O. Yesharim, J. Foley-Comer, S. Trajtenberg-Mills, D. Freedman, A. M. Bronstein, A. Arie, Inverse design of spontaneous parametric downconversion for generation of high-dimensional qudits, Optica 9, 602-615, 2022 details
N. Talati, H. Ye, S. Vedula, K.-Y. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw, A. M. Bronstein, T. Mudge, R. Dreslinski, Mint: An Accelerator For Mining Temporal Motifs, Proc. MICRO, 2022 details
E. Zheltonozhskii, C. Baskin, A. Mendelson, A. M. Bronstein, O. Litany, Contrast to divide: Self-supervised pre-training for learning with noisy labels, Proc. of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022 details
G. Pai, A. Bronstein, R. Talmon, R. Kimmel, Deep isometric maps, Image and Vision Computing, 2022 details
N. Diamant, N. Shandor, A. M. Bronstein, Delta-GAN-Encoder: Encoding semantic changes for explicit image editing, using few synthetic samples, arXiv:2111.08419, 2022 details
T. Blau, R. Ganz, B. Kawar, A. M. Bronstein, M. Elad , Threat model-agnostic adversarial defense using diffusion models, arXiv preprint arXiv:2207.08089, 2022 details
A. Arbelle, S. Doveh, A. Alfassy, J. Shtok, G. Lev, E. Schwartz, H. Kuehne, H. Barak Levi, P. Sattigeri, R. Panda, C.-F. Chen, A. M. Bronstein, K. Saenko, S. Ullman, R. Giryes, R. Feris, L. Karlinsky, Detector-free weakly supervised grounding by separation, Proc. CVPR, 2022 details
D. E. Fordham, D. Rosentraub, A. L. Polsky, T. Aviram, Y. Wolf, O. Perl, A. Devir, S. Rosentraub, D. H. Silver, Y. Gold Zamir, A. M. Bronstein, M. Lara Lara, J. Ben Nagi, A. Alvarez, S. Munné, Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?, Human Reproduction, Volume 37, Issue 10, Pages 2275–2290, 2022 details
S. Doveh, E. Schwartz, C. Xue, R. Feris, A. M. Bronstein, R. Giryes, L. Karlinsky, MetAdapt: Meta-learned task-adaptive architecture for few-shot classification, Pattern Recognition Letters, 2021 details
T. Weiss, N. Peretz, S. Vedula, A. Feuer, A. M. Bronstein, Joint optimization of system design and reconstruction in MIMO radar imaging, Proc. IEEE Int'l Workshop on Machine Learning for Signal Processing, 2021 details
Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, Loss aware post-training quantization, Machine Learning, 2021 details
C. Baskin, B. Chmiel, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, CAT: Compression-aware training for bandwidth reduction, JMLR, 2021 details
E. Amrani, A. M. Bronstein, Self-supervised classification network, Proc. ECCV, 2022 details
E. Rozenberg, D. Freedman, A. M. Bronstein, Learning to localize objects using limited annotation with applications to thoracic diseases, IEEE Access Vol. 9, 2021 details
T. Weiss, O. Senouf, S. Vedula, O. Michailovich, M. Zibulevsky, A. M. Bronstein, PILOT: Physics-Informed Learned Optimal Trajectories for accelerated MRI, Journal of Machine Learning for Biomedical Imaging (MELBA), 2021 details
Y. Elul, A. Rosenberg, A. Schuster, A. M. Bronstein, Y. Yaniv, Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis, Proc. US National Academy of Sciences (PNAS), 2021 details
L. Karlinsky, J. Shtok, A. Alfassy, M. Lichtenstein, S. Harary, E. Schwartz, S. Doveh, P. Sattigeri, R. Feris, A. M. Bronstein, R. Giryes, StarNet: towards weakly supervised few-shot detection and explainable few-shot classification, Proc. AAAI, 2021 details
E. Amrani, R. Ben-Ari, D. Rotman, A. M. Bronstein, Noise estimation using density estimation for self-supervised multimodal learning, Proc. AAAI, 2021 details
O. Dahary, M. Jacoby, A. M. Bronstein, Digital Gimbal: End-to-end deep image stabilization with learnable exposure times, Proc. CVPR, 2021 details
A. Boyarski, S. Vedula, A. M. Bronstein, Spectral geometric matrix completion, Proc. Mathematical and Scientific Machine Learning, 2021 details
E. Rozenberg, A. Karnieli, O. Yesharim, S. Trajtenberg-Mills, D. Freedman, A. M. Bronstein, A. Arie, Inverse design of quantum holograms in three-dimensional nonlinear photonic crystals, CLEO, 2021 details
A. Karbachevsky, C. Baskin, E. Zheltonozshkii, Y. Yermolin, F. Gabbay, A. M. Bronstein, A. Mendelson, Early-stage neural network hardware performance analysis, Sustainability 13(2):717, 2021 details
C. Baskin, E. Schwartz, E. Zheltonozhskii, N. Liss, R. Giryes, A. M. Bronstein, A. Mendelson, UNIQ: Uniform noise injection for non-uniform quantization of neural networks, ACM Transactions on Computer Systems (TOCS), 2020 details
B. Finkelshtein, C. Baskin, E. Zheltonozhskii, U. Alon, Single-node attack for fooling graph neural networks, arXiv:2011.03574, 2020 details
J. Alush-Aben, L. Ackerman-Schraier, T. Weiss, S. Vedula, O. Senouf, A. M. Bronstein, 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI, Proc. Machine Learning for Medical Image Reconstruction, MICCAI 2020 details
T. Weiss, S. Vedula, O. Senouf, O. Michailovich, A. M. Bronstein, Towards learned optimal q-space sampling in diffusion MRI, Proc. Computational Diffusion MRI, MICCAI 2020 details
E. Zheltonozhskii, C. Baskin, A. M. Bronstein, A. Mendelson, Self-supervised learning for large-scale unsupervised image clustering, NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice, 2020 details
G. Mariani, L. Cosmo, A. M. Bronstein, E. Rodolà, Generating adversarial surfaces via band-limited perturbations, Computer Graphics Forum, 2020 details
B. Chmiel, C. Baskin, R. Banner, E. Zheltonozshkii, Y. Yermolin, A. Karbachevsky, A. M. Bronstein, A. Mendelson, Feature map transform coding for energy-efficient CNN inference, Proc. Intl. Joint Conf. on Neural Networks (IJCNN), 2020 details
E. Amrani, R. Ben-Ari, T. Hakim, A. M. Bronstein, Self-Supervised Object Detection and Retrieval Using Unlabeled Videos, CVPR workshop, 2020 details
D. H. Silver, M. Feder, Y. Gold-Zamir, A. L. Polsky, S. Rosentraub, E. Shachor, A. Weinberger, P. Mazur, V. D. Zukin, A. M. Bronstein, Data-driven prediction of embryo implantation probability using IVF time-lapse imaging, Proc. MIDL, 2020 details
T. Weiss, S. Vedula, O. Senouf, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Joint learning of Cartesian undersampling and reconstruction for accelerated MRI, Proc. Int’l Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2020 details
S. Sommer, A. M. Bronstein, Horizontal flows and manifold stochastics in geometric deep learning, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2020 details
K. Rotker, D. Ben-Bashat, A. M. Bronstein, Over-parameterized models for vector fields, SIAM Journal on Imaging Sciences (SIIMS), 2020 details
A. Tsitsulin, M. Munkhoeva, D. Mottin, P. Karras. A. M. Bronstein, I. Oseledets, E. Müller, Intrinsic multi-scale evaluation of generative models, Proc. ICLR, 2020 details
A. Karbachevsky, C. Baskin, E. Zheltonozshkii, Y. Yermolin, F. Gabbay, A. M. Bronstein, A. Mendelson, HCM: Hardware-aware complexity metric for neural network architectures, arXiv:2004.08906, 2020 details
E. Zheltonozhskii, C. Baskin, Y. Nemcovsky, B. Chmiel, A. Mendelson, A. M. Bronstein, Colored noise injection for training adversarially robust neural networks, arXiv:2003.02188, 2020 details
A. Livne, A. M. Bronstein, R. Kimmel, Z. Aviv, S. Grofit, Do we need depth in state-of-the-art face authentication?, Proc. IEEE Int'l Conf. on 3D Vision (3DV), 2020 details
M. Shkolnik, B. Chmiel, R. Banner, G. Shomron, Y. Nahshan, A. M. Bronstein, U. Weiser, Robust Quantization: One Model to Rule Them All, Proc. NeurIPS, 2020 details
Y. Choukroun , A. Shtern, A. M. Bronstein, R. Kimmel, Hamiltonian operator for spectral shape analysis, IEEE Trans. Vis. and Comp. Graphics, vol. 26(2), 2020 details
A. Boyarski, S. Vedula, A. M. Bronstein, Deep matrix factorization with spectral geometric regularization, arXiv: 1911.07255, 2019 details
Y. Nahshan, B. Chmiel, C. Baskin, E. Zheltonozhskii, R. Banner, A. M. Bronstein, A. Mendelson, Loss aware post-training quantization, arXiv: 1911.07190, 2019 details
Y. Nemcovsky, E. Zheltonozhskii, C. Baskin, B. Chmiel, A. M. Bronstein, A. Mendelson, Smoothed inference for adversarially-trained models, arXiv: 1911.07198, 2019 details
S. Doveh, E. Schwartz, C. Xue, R. Feris, A. M. Bronstein, R. Giryes, L. Karlinsky, MetAdapt: Meta-learned task-adaptive architecture for few-shot classification, arXiv: 1912.00412, 2019 details
E. Rozenberg, D. Freedman, A. M. Bronstein, Localization with limited annotation for chest X-rays, Proc. ML4H, NeurIPS, 2019 details
S. Vedula, O. Senouf, G. Zurakov, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Learning beamforming in ultrasound imaging, Proc. Medical Imaging with Deep Learning (MIDL), 2019 details
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, R. Feris, A. Kumar, R. Giryes, A. M. Bronstein, RepMet: Representative-based metric learning for classification and one-shot object detection, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
O. Halimi, O. Litany, E. Rodolà, A. M. Bronstein, R. Kimmel, Self-supervised learning of dense shape correspondence, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, A. M. Bronstein, LaSO: Label-Set Operations networks for multi-label few-shot learning, Proc. Computer Vision and Pattern Recognition (CVPR), 2019 details
A. Zabatani, V. Surazhsky, E. Sperling, S. Ben Moshe, O. Menashe, D. H. Silver, Z. Karni, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Intel RealSense SR300 Coded light depth Camera, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2019 details
Y. Zur, C. Baskin, E. Zheltonozhskii, B. Chmiel, I. Evron, A. M. Bronstein, A. Mendelson, Towards learning of filter-level heterogeneous compression of convolutional neural networks, Proc. AutoML Workshop, Int'l Conf. on Machine Learning (ICML), 2019 details
E. Schwartz, L. Karlinsky, R. Feris, R. Giryes, A. M. Bronstein, Baby steps towards few-shot learning with multiple semantics, arXiv:1906.01905, 2019 details
A. Rampini, I. Tallini, M. Ovsjanikov, A. M. Bronstein, E. Rodola, Correspondence-free region localization for partial shape similarity via Hamiltonian spectrum alignment, Proc. 3D Vision (3DV), 2019 (Best paper award) details
O. Senouf, S. Vedula, T. Weiss, A. M. Bronstein, O. Michailovich, M. Zibulevsky, Self-supervised learning of inverse problem solvers in medical imaging, Proc. Medical Image Learning with Less Labels and Imperfect Data, MICCAI 2019 details
R. M. Dyke, C Stride, Y.-K. Lai, P. L. Rosin, M. Aubry, A. Boyarski, A. M. Bronstein, M. M. Bronstein, D. Cremers, M. Fisher, T. Groueix, D. Guo, V. G. Kim, R. Kimmel, Z. Lähner, K. Li, O. Litany, T. Remez, E. Rodolà, B. C. Russell, Y. Sahillioglu, R. Slossberg, M. Vestner, Z. Wu, J. Yang, Gary Tam, Shape Correspondence with Isometric and Non-Isometric Deformations, Eurographics Workshop on 3D Object Retrieval, 2019 details
N. Diamant, D. Zadok, C. Baskin, E. Schwartz, A. M. Bronstein, Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level, Proc. Int'l Conf. on Image Processing (ICIP), 2019 details
G. Pai, R. Talmon, A. M. Bronstein, R. Kimmel, DIMAL: Deep isometric manifold learning using sparse geodesic sampling, Proc. IEEE Winter Conf. on Applications of Computer Vision (WACV), 2019 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, D. Cremers, Partial single- and multi-shape dense correspondence using functional maps, Chapter in The Handbook of Numerical Analysis - Processing, Analyzing and Learning of Images, Shapes, and Forms, Elsevier, 2019 details
A. Boyarski, A. M. Bronstein, Multidimensional scaling, Computer Vision: A Reference Guide, (Katsushi Ikeuchi, Ed.) details
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, R. Feris, A. Kumar, R. Giryes, A. M. Bronstein, ∆-encoder: an effective sample synthesis method for few-shot object recognition, Proc. Neural Information Processing Systems (NIPS), 2018 details
E. Rodolà, Z. Lähner, A. M. Bronstein, M. M. Bronstein, J. Solomon, Functional maps representation on product manifolds, arXiv:1809.10940, 2018 details
C. Baskin, N. Liss, Y. Chai, E. Zheltonozhskii, E. Schwartz, R. Giryes, A. Mendelson, A. M. Bronstein, NICE: noise injection and clamping estimation for neural network quantization, arXiv:1810.00162, 2018 details
Q. Qiu, J. Lezama, A. M. Bronstein, G. Sapiro, ForestHash: Semantic hashing with shallow random forests and tiny convolutional networks, Proc. European Conf. on Computer Vision (ECCV), 2018 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Class-aware fully-convolutional Gaussian and Poisson denoising, IEEE Trans. Image Processing, Vol. 27(11), 2018 details
A. Tsitsulin, D. Mottin, P. Karras, A. M. Bronstein, E, Mueller, NetLSD: Hearing the shape of a graph, Proc. ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2018 details
O. Senouf, S. Vedula, G. Zurakhov, A. M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Blondheim, High frame-rate cardiac ultrasound imaging with deep learning, Proc. Int'l Conf. Medical Image Computing & Computer Assisted Intervention (MICCAI), 2018 details
S. Vedula, O. Senouf, G. Zurakhov, A. M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Gaitini, High quality ultrasonic multi-line transmission through deep learning, Proc. Machine Learning for Medical Image Reconstruction (MLMIR), 2018 details
A. Tsitsulin, D. Mottin, P. Karras, A. M. Bronstein, E, Mueller, SGR: Self-supervised spectral graph representation learning, Proc. KDD Deep Learning Day, 2018 details
E. Schwartz, R. Giryes, A. M. Bronstein, DeepISP: Towards learning an end-to-end image processing pipeline, IEEE Trans. on Image Processing, 2018 details
H. Haim, S. Elmalem, R. Giryes, A. M. Bronstein, E. Marom, Depth estimation from a single image using deep learned phase coded mask, IEEE Trans. Computational Imaging, Vol. 2(3), 2018 (Winner of the OSA Student Grand Challenge The Optical System of the Future) details
E. Tsitsin, A. M. Bronstein, T. Hendler, M. Medvedovsky, Passive electric impedance tomography, Proc. Electric Impedance Tomography (EIT), 2018 details
E. Tsitsin, T. Mund, A. M. Bronstein, Printable anisotropic phantom for EEG with distributed current sources, Proc. IEEE Int'l Symposium on Biomedical Imaging (ISBI), 2018 details
E. Tsitsin, M. Medvedovsky, A. M. Bronstein, VibroEEG: Improved EEG source reconstruction by combined acoustic-electric imaging, Proc. IEEE Int'l Symposium on Biomedical Imaging (ISBI), 2018 details
C. Baskin, N. Liss, E. Zheltonozhskii, A. M. Bronstein, A. Mendelson, Streaming architectures for large-scale quantized neural networks on an FPGA-based dataflow platform, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018 details
R. Giryes, Y. C. Eldar, A. M. Bronstein, G. Sapiro, Tradeoffs between convergence speed and reconstruction accuracy in inverse problems, IEEE Trans. on Signal Processing, Vol. 66(7), 2018 details
S. Vedula, O. Senouf, A. M. Bronstein, O. V. Michailovich, M. Zibulevsky, Towards CT-quality ultrasound imaging using deep learning, arXiv:1710.06304, 2017 details
O. Litany, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, Deep Functional Maps: Structured prediction for dense shape correspondence, Proc. Int'l Conf. on Computer Vision (ICCV), 2017 details
Z. Laehner, M. Vestner, A. Boyarski, O. Litany, R. Slossberg, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, R. Kimmel, D. Cremers, Efficient deformable shape correspondence via kernel matching, Proc. 3D Vision (3DV), 2017 details
G. Alexandroni, Y. Podolsky, H. Greenspan, T. Remez, O. Litany, A. M. Bronstein, R. Giryes, White matter fiber representation using continuous dictionary learning, Proc. Int'l Conf. Medical Image Computing & Computer Assisted Intervention (MICCAI), 2017 details
M. Vestner, R. Litman, E. Rodolà, A. M. Bronstein, D. Cremers, Product Manifold Filter: Non-rigid shape correspondence via kernel density estimation in the product space, Proc. Computer Vision and Pattern Recognition (CVPR), 2017 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, Fully spectral partial shape matching, Computer Graphics Forum, Vol. 36(2), 2017 details
A. Boyarski, A. M. Bronstein, M. M. Bronstein, Subspace least squares multidimensional scaling, Proc. Scale Space and Variational Methods (SSVM), 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep class-aware image denoising, Proc. Int'l Conf. on Image Processing (ICIP), 2017 details
O. Litany, T. Remez, A. M. Bronstein, Cloud Dictionary: Sparse coding and modeling for point clouds, arXiv:1612.04956, 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep class-aware denoising, arXiv:1701.01698, 2017 details
T. Remez, O. Litany, R. Giryes, A. M. Bronstein, Deep convolutional denoising of low-light images, arXiv:1701.01687, 2017 details
O. Litany, T. Remez, D. Freedman, L. Shapira, A. M. Bronstein, R. Gal, ASIST: Automatic Semantically Invariant Scene Transformation, Computer Vision and Image Understanding, Vol. 157, 2017 details
M. Ovsjanikov, E. Corman, M. M. Bronstein, E. Rodolà, M. Ben-Chen, L. Guibas, F. Chazal, A. M. Bronstein, Computing and processing correspondences with functional maps, SIGGRAPH Courses, 2017 details
Y. Choukroun, A. Shtern, A. M. Bronstein, R. Kimmel, Hamiltonian operator for spectral shape analysis, arXiv:1611.01990, 2016 details
A. M. Bronstein, Y. Choukroun, R. Kimmel, M. Sela, Consistent discretization and minimization of the L1 norm on manifolds, Proc. 3D Vision (3DV), 2016 details
R. Litman, A. M. Bronstein, SpectroMeter: Amortized sublinear spectral approximation of distance on graphs, Proc. 3D Vision (3DV), 2016 details
T. Remez, O. Litany, S. Yoseff, H. Haim, A. M. Bronstein, FPGA system for real-time computational extended depth of field imaging using phase aperture coding, arXiv:1608.01074, 2016 details
R. Giryes, G. Sapiro, A. M. Bronstein, Deep neural networks with random Gaussian weights: A universal classification strategy?, IEEE Trans. Signal Processing, Vol. 64(13), 2016 details
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, D. Cremers, Non-rigid puzzles, Computer Graphics Forum, Vol. 35(5), 2016 (SGP Best Paper Award) details
X. Bian, H. Krim, A. M. Bronstein, L. Dai, Sparsity and nullity: paradigms for analysis dictionary learning, SIAM J. Imaging Sci., Vol. 9(3), 2016 details
D. Pickup, X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono, A. Ben Hamza, A. M. Bronstein, M. M. Bronstein, S. Bu, U. Castellani, S. Cheng, V. Garro, A. Giachetti, A. Godil, J. Han, H. Johan, L. Lai, B. Li, C. Li, H. Li, R. Litman, X. Liu, Z. Liu, Y. Lu, A. Tatsuma, J. Ye, Shape retrieval of non-rigid 3D human models, Intl. Journal of Computer Vision (IJCV), 2016 details
O. Litany, T. Remez, A. M. Bronstein, Image reconstruction from dense binary pixels, arXiv:1512.01774, 2015
D. Eynard, A. Kovnatsky, M. M. Bronstein, K. Glashoff, A. M. Bronstein, Multimodal manifold analysis using simultaneous diagonalization of Laplacians, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37(12), 2015 details
T. Remez, O. Litany, A. M. Bronstein, A Picture is Worth a Billion Bits: Real-time image reconstruction from dense binary pixels, arXiv:1510.04601, 2015 details
A. M. Bronstein, New dimensions of media, Universidad La Salle, Revista de ciencias de la computación, Vol. 3(1), 2015
H. Haim, A. M. Bronstein, E. Marom, Computational all-in-focus imaging using an optical phase mask, OSA Optics Express, Vol. 23(19), 2015 details
R. Litman, S. Korman, A. M. Bronstein, S. Avidan, GMD: Global model detection via inlier rate estimation, Proc. Computer Vision and Pattern Recognition (CVPR), 2015 details
I. Sipiran, B. Bustos, T. Schreck, A. M. Bronstein, M. M. Bronstein, U. Castellani, S. Choi, L. Lai, H. Li, R. Litman, L. Sun, SHREC'15 Track: Scalability of non-rigid 3D shape retrieval, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2015 details
X. Bian, H. Krim, A. M. Bronstein, L. Dai, Sparse null space basis pursuit and analysis dictionary learning for high-dimensional data analysis, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2015 details
Y. Aflalo, A. M. Bronstein, R. Kimmel, On convex relaxation of graph isomorphism, Proc. US National Academy of Sciences (PNAS), 2015 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Learning efficient sparse and low-rank models, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37(9), 2015 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Supervised non-negative matrix factorization for audio source separation, Chapter in Excursions in Harmonic Analysis (R. Balan, M. Begue, J. J. Benedetto, W. Czaja, K. Okoudjou Eds.), Birkhaeuser, 2015 details
Q. Qiu, G. Sapiro, A. M. Bronstein, Random forests can hash, arXiv:1412.5083, 2014 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Supervised non-Euclidean sparse NMF via bilevel optimization with applications to speech enhancement, Proc. Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014 details
S. Korman, R. Litman, S. Avidan, A. M. Bronstein, Probably approximately symmetric: Fast rigid symmetry detection with global guarantees, Computer Graphics Forum (CGF), Vol. 34(1), 2014 details
R. Litman, A. M. Bronstein, M. M. Bronstein, U. Castellani, Supervised learning of bag-of-features shape descriptors using sparse coding, Computer Graphics Forum (CGF), Vol. 33(5), 2014 details
O. Menashe, A. M. Bronstein, Real-time compressed imaging of scattering volumes, Proc. Int'l Conf. on Image Processing (ICIP), 2014 details
S. Biasotti, A. Cerri, A. M. Bronstein, M. M. Bronstein, Quantifying 3D shape similarity using maps: Recent trends, applications and perspectives, Proc. EUROGRAPHICS STARS, 2014 details
J. Masci, M. M. Bronstein, A. M. Bronstein, J. Schmidhuber, Multimodal similarity-preserving hashing, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(4), 2014 details
J. Masci, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro, Sparse similarity-preserving hashing, Proc. Int'l Conf. on Learning Representations (ICLR), 2014 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Equi-affine invariant intrinsic geometries for bendable shapes analysis, Journal of Mathematical Imaging and Vision (JMIV), Vol. 50(1), 2014 details
D. Pickup, X. Sun, P. L. Rosin, R. R. Martin, Z. Cheng, Z. Lian, M. Aono, A. Ben Hamza, A. M. Bronstein, M. M. Bronstein, S. Bu, U. Castellani, S. Cheng, V. Garro, A. Giachetti, A. Godil, J. Han, H. Johan, L. Lai, B. Li, C. Li, H. Li, R. Litman, X. Liu, Z. Liu, Y. Lu, A. Tatsuma, J. Ye, Shape retrieval of non-rigid 3D human models, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2014 details
P. Sprechmann, R. Litman, T. Ben Yakar, A. M. Bronstein, G. Sapiro, Efficient supervised sparse analysis and synthesis operators, Proc. Neural Information Proc. Systems (NIPS), 2013 details
T. Ben Yakar, R. Litman, P. Sprechmann, A. M. Bronstein, G. Sapiro, Bilevel sparse models for polyphonic music transcription, Proc. Annual Conf. of the Int'l Society for Music Info. Retrieval (ISMIR), 2013 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, G. Sapiro, Sparse modeling of intrinsic correspondences, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, K. Glashoff, R. Kimmel, Coupled quasi-harmonic bases, Computer Graphics Forum (CGF), Vol. 32(2), 2013 details
P. Sprechmann, A. M. Bronstein, J.-M. Morel, G. Sapiro, Audio restoration from multiple copies, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details
P. Sprechmann, A. M. Bronstein, M. M. Bronstein, G. Sapiro, Learnable low rank sparse models for speech denoising, Proc. Int'l Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2013 details
A. Kovnatski, D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Geometric and photometric data fusion in non-rigid shape analysis, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, Partial shape matching without point-wise correspondence, Numerical Mathematics: Theory, Methods and Applications (NM-TMA), Vol. 6(1), 2013 details
R. Litman, and A. M. Bronstein, Learning spectral descriptors for deformable shape correspondence, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 36(1), 2013 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Real-time online singing voice separation from monaural recordings using robust low-rank modeling, Proc. Annual Conference of the Int'l Society for Music Information Retrieval (ISMIR), 2012 (Best poster presentation award) details
O. Litany, A. M. Bronstein, M. M. Bronstein, Putting the pieces together: regularized multi-shape partial matching, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
A. Kovnatsky, A. M. Bronstein, M. M. Bronstein, Stable spectral mesh filtering, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
I. Kokkinos, M. M. Bronstein, R. Litman, A. M. Bronstein, Intrinsic shape context descriptors for deformable shapes, Proc. Computer Vision and Pattern Recognition (CVPR), 2012 details
E. Rodolà, A. M. Bronstein, A. Albarelli, F. Bergamasco, A. Torsello, A game-theoretic approach to deformable shape matching, Proc. Computer Vision and Pattern Recognition (CVPR), 2012 details
M. Spagnuolo, M. M. Bronstein, A. M. Bronstein, A. Ferreira (Eds.), Eurographics Workshop on 3D Object Retrieval, Eurographics Association, 2012, ISBN: 978-3-905674-36-1 details
R. Litman, A. M. Bronstein, M. M. Bronstein, Stable volumetric features in deformable shapes, Computers and Graphics (CAG), Vol. 36(5), 2012 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, X.-C. Tai, R. Kimmel, Group-valued regularization for analysis of articulated motion, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2012 details
P. Sprechmann, A. M. Bronstein, G. Sapiro, Learning efficient structured sparse models, Proc. Int'l Conf. on Machine Learning (ICML), 2012 details
A. Zabatani, A. M. Bronstein, Parallelized algorithms for rigid surface alignment on GPU, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Articulated motion segmentation of point clouds by group-valued regularization, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, D. Raviv, R. Kimmel, Affine-invariant photometric heat kernel signatures, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2012 details
C. Strecha, A. M. Bronstein, M. M. Bronstein, P. Fua, LDAHash: improved matching with smaller descriptors, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 34(1), 2012 details
B. M. Bruckstein, B. ter haar Romeny, A. M. Bronstein, M. M. Bronstein (Eds.), Scale Space and Variational Methods in Computer Vision, Lecture Notes in Computer Science (LNCS) No. 6667, Springer, 2012, ISBN: 978-3-642-24784-2 details
R. Litman, A. M. Bronstein, M. M. Bronstein, Stable semi-local features for non-rigid shapes, Chapter in Innovations for Shape Analysis: Models and Algorithms (M. Breuss, A. M. Bruckstein, P. Maragos Eds.), Springer, 2012 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, A. Wolf, R. Kimmel, Group-valued regularization for motion segmentation of articulated shapes, Chapter in Innovations for Shape Analysis: Models and Algorithms (M. Breuss, A. M. Bruckstein, P. Maragos Eds.), Springer, 2012 details
A. M. Bronstein, M. M. Bronstein, M. Ovsjanikov, 3D features, surface descriptors, and object descriptors, Chapter in 3D Imaging, Analysis and Applications (N. Pears, Y. Liu, P. Bunting, Eds.), Springer, 2012. details
R. Kimmel, C. Zhang, A. M. Bronstein, M. M. Bronstein, Are MSER features really interesting?, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 33(11), 2011 details
A. M. Bronstein, Spectral descriptors for deformable shapes, arXiv:1110.5015, 2011 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant diffusion geometry for the analysis of deformable 3D shapes, Proc. Computer Vision and Pattern Recognition (CVPR), 2011 details
M. M. Bronstein, A. M. Bronstein, Shape recognition with spectral distances, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 33(5), 2011 details
J. Pokrass, A. M. Bronstein, M. M. Bronstein, A correspondence-less approach to matching of deformable shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Photometric heat kernel signatures, Proc. Scale Space and Variational Methods (SSVM), 2011 details
J. Aflalo, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Deformable shape retrieval by learning diffusion kernels, Proc. Scale Space and Variational Methods (SSVM), 2011 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, A. Wolf, R. Kimmel, Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
C. Wang, M. M. Bronstein, A. M. Bronstein, N. Paragios, Discrete minimum distortion correspondence problems for non-rigid shape matching, Proc. Scale Space and Variational Methods (SSVM), 2011 details
A. Hooda, M. M. Bronstein, A. M. Bronstein, R. Horaud, Shape palindromes: analysis of intrinsic symmetries in 2D articulated shapes, Proc. Scale Space and Variational Methods (SSVM), 2011 details
F. Michel, M. M. Bronstein, A. M. Bronstein, N. Paragios, Boosted metric learning for 3D multi-modal deformable registration, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2011 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant geodesic geometry of deformable 3D shapes, Computers and Graphics (CAG), Vol. 35(3), 2011 details
R. Litman, A. M. Bronstein, A. M. Bronstein, Diffusion-geometric maximally stable component detection in deformable shapes, Computers and Graphics (CAG), Vol. 35(3), 2011 details
A. M. Bronstein, M. M. Bronstein, M. Ovsjanikov, L. J. Guibas, Shape Google: geometric words and expressions for invariant shape retrieval, ACM Trans. Graphics (TOG), Vol. 30(1), 2011 details
A. M. Bronstein, M. M. Bronstein, Metric approaches to invariant shape similarity, Chapter in Handbook of Mathematical Methods in Imaging (O. Scherzer Ed.), Springer, 2011 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant geodesic geometry of deformable 3D shapes, arXiv:1012.5936, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen, Affine-invariant diffusion geometry for the analysis of deformable 3D shapes, arXiv:1012.5933, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Full and partial symmetries of non-rigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 89(1), 2010 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, M. Mahmoudi, G. Sapiro, A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching, Int'l Journal of Computer Vision (IJCV), Vol. 89(2), 2010 details
N. Mitra, A. M. Bronstein, M. M. Bronstein, Intrinsic regularity detection in 3D geometry, Proc. European Conf. Computer Vision (ECCV), 2010 details
A. M. Bronstein, M. M. Bronstein, Spatially-sensitive affine-invariant image descriptors, Proc. European Conf. Computer Vision (ECCV), 2010 details
M. M. Bronstein, A. M. Bronstein, F. Michel, N. Paragios, Data fusion through cross-modality metric learning using similarity-sensitive hashing, Proc. Computer Vision and Pattern Recognition (CVPR), 2010 details
D. Raviv, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Volumetric heat kernel signatures, Proc. Int'l Workshop on 3D Object Retrieval (3DOR), ACM Multimedia, 2010 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, G. Sapiro, Diffusion symmetries of non-rigid shapes, Proc. Int'l Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2010 details
G. Rosman, M. M. Bronstein, A. M. Bronstein, R. Kimmel, Nonlinear dimensionality reduction by topologically constrained isometric embedding, Intl. Journal of Computer Vision (IJCV), Vol. 89(1), 2010 details
A. M. Bronstein, M. M. Bronstein, U. Castellani, B. Falcidieno, A. Fusiello, A. Godil, L. J. Guibas, I. Kokkinos, Z. Lian, M. Ovsjanikov, G. Patané, M. Spagnuolo, R. Toldo, SHREC 2010: robust large-scale shape retrieval benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
A. M. Bronstein, M. M. Bronstein, B. Bustos, U. Castellani, M. Crisani, B. Falcidieno, L. J. Guibas, I. Kokkinos, V. Murino, M. Ovsjanikov, G. Patané, I. Sipiran, M. Spagnuolo, J. Sun, SHREC 2010: robust feature detection and description benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
A. M. Bronstein, M. M. Bronstein, U. Castellani, A. Dubrovina, L. J. Guibas, R. P. Horaud, R. Kimmel, D. Knossow, E. von Lavante, D. Mateus, M. Ovsjanikov, A. Sharma, SHREC 2010: robust correspondence benchmark, Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010 details
O. Rubinstein, Y. Honen, A. M. Bronstein, M. M. Bronstein, R. Kimmel, 3D color video camera, Proc. Workshop on 3D Digital Imaging and Modeling (3DIM), 2009 details
M. Ovsjanikov, A. M. Bronstein, M. M. Bronstein, L. Guibas, ShapeGoogle: a computer vision approach for invariant shape retrieval, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009 details
Y. Devir, G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, On reconstruction of non-rigid shapes with intrinsic regularization, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Topology-invariant similarity of nonrigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 81(3), 2009 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Partial similarity of objects, or how to compare a centaur to a horse, Int'l Journal of Computer Vision (IJCV), Vol. 84(2), 2009 details
A. M. Bronstein, M. M. Bronstein, Y. Carmon, R. Kimmel, Partial similarity of shapes using a statistical significance measure, IPSJ Trans. Computer Vision and Application, Vol. 1, 2009 details
O. Weber, Y. Devir, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Parallel algorithms for approximation of distance maps on parametric surfaces, ACM Trans. on Graphics, Vol. 27(4), 2008 details
A. M. Bronstein, M. M. Bronstein, Regularized partial matching of rigid shapes, Proc. European Conf. on Computer Vision (ECCV), 2008 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Analysis of two-dimensional non-rigid shapes, Int'l Journal of Computer Vision (IJCV), Vol. 78(1), 2008 details
A. M. Bronstein, M. M. Bronstein, Not only size matters: regularized partial matching of nonrigid shapes, Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Registration (NORDIA), 2008 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Numerical geometry of non-rigid shapes, Springer, 2008, ISBN: 978-0387733005 details
G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Topologically constrained isometric embedding, Human Motion Understanding, Modeling, Capture, and Animation, Computational Imaging and Vision, Vol. 36, Springer, 2008 details
R. Giryes, A. M. Bronstein, Y. Moshe, M. M. Bronstein, Embedded system for 3D shape reconstruction, Proc. European DSP Education and Research Symposium (EDERS), 2008 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Calculus of non-rigid surfaces for geometry and texture manipulation, IEEE Trans. Visualization and Computer Graphics, Vol 13(5), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes, Proc. Int'l Conf. Computer Vision (ICCV), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Weighted distance maps computation on parametric three-dimensional manifolds, Journal of Computational Physics, Vol. 255(1), 2007 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Paretian similarity for partial comparison of non-rigid objects, Proc. Scale Space and Variational Methods in Computer Vision (SSVM), 2007 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Partial similarity of objects and text sequences, Proc. Information Theory and Applications Workshop, 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-invariant representation of faces, IEEE Trans. Image Processing, Vol. 16(1), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Story of Cinderella: biometrics and isometry-invariant distances, Chapter in 3D Imaging for Safety and Security (A. Koschan, M. Pollefeys, M. Abidi Eds.), Springer, 2007 details
D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, Symmetries of non-rigid shapes, Proc. Workshop on Non-rigid Registration and Tracking through Learning (NRTL), 2007 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Robust expression-invariant face recognition from partially missing data, Proc. European Conf. on Computer Vision (ECCV), 2006 details
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, Matching two-dimensional articulated shapes using generalized multidimensional scaling, Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Face2Face: an isometric model for facial animation, Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Efficient computation of isometry-invariant distances between surfaces, SIAM J. Scientific Computing, Vol. 28(5), 2006
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, On separation of semitransparent dynamic images from static background, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2006 details
M. M. Bronstein, A. M. Bronstein, R. Kimmel, I. Yavneh, Multigrid multidimensional scaling, Numerical Linear Algebra with Applications (NLAA), Vol. 13(2), 2006 (Special issue on multigrid methods) details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching, Proc. US National Academy of Sciences (PNAS), Vol. 103(5), 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression invariant face recognition: faces as isometric surfaces, Chapter in Face Processing: Advanced Modeling and Methods (Rama Chellappa, Wenyi Zhao Eds.), Academic Press, 2006 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Three-dimensional face recognition, Int'l Journal of Computer Vision (IJCV), Vol. 64(1), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Quasi maximum likelihood blind deconvolution: super- an sub-Gaussianity versus consistency, IEEE Trans. Signal Processing, Vol. 53(7), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Relative optimization for blind deconvolution, IEEE Trans. on Signal Processing, Vol. 53(6), 2005 details
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Blind deconvolution of images using optimal sparse representations, IEEE Trans. on Image Processing, Vol. 14(6), 2005 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Expression-invariant face recognition via spherical embedding, Proc. Int'l Conf. on Image Processing (ICIP), 2005 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Unmixing tissues: sparse component analysis in multi-contrast MRI, Proc. Int'l Conf. on Image Processing (ICIP), 2005 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, Isometric embedding of facial surfaces into S^3, Proc. Int'l Conf. on Scale Space and PDE Methods in Computer Vision (SSVM), 2005 details
M. M. Bronstein, A. M. Bronstein, R. Kimmel, I. Yavneh, A multigrid approach for multi-dimensional scaling, Proc. Copper Mountain Conf. Multigrid Methods, 2005 (Best Paper Award) details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Sparse ICA for blind separation of transmitted and reflected images, Int'l Journal of Imaging Science and Technology (IJIST), Vol. 15(1), 2005 details
A. M. Bronstein, M. M. Bronstein, E. Gordon, R. Kimmel, Fusion of 2D and 3D data in three-dimensional face recognition, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,, Optimal sparse representations for blind source separation and blind deconvolution: a learning approach, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Fast relative Newton algorithm for blind deconvolution of images, Proc. Int'l Conf. on Image Processing (ICIP), 2004 details
A. M. Bronstein, M. M. Bronstein, R. Kimmel, A. Spira, Face recognition from facial surface metric, Proc. European Conf. on Computer Vision (ECCV), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Blind source separation using block-coordinate relative Newton method, Signal Processing, Vol. 84(8), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Blind source separation using the block-coordinate relative Newton method, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, Lecture Notes in Comp. Science No. 3195, Springer, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, QML blind deconvolution: asymptotic analysis, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Optimal sparse representations for blind deconvolution of images, Proc. Int'l Conf. on Independent Component Analysis and Blind Signal Separation, 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Quasi maximum likelihood blind deconvolution of images acquired through scattering media, Proc. Int'l Symposium on Biomedical Imaging (ISBI), 2004 details
A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Optimal nonlinear line-of-flight estimation in positron emission tomography, IEEE Trans. on Nuclear Science, Vol. 50(3), 2003 details
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The demand for running NNs in embedded environments has increased significantly in recent years due to the significant success of convolutional neural network (CNN) approaches in various tasks, including image recognition and generation. The task of achieving high accuracy on resource-restricted devices, however, is still considered to be challenging, which is mainly due to the vast number of design parameters that need to be balanced. While the quantization of CNN parameters leads to a reduction of power and area, it can also generate unexpected changes in the balance between communication and computation. This change is hard to evaluate, and the lack of balance may lead to lower utilization of either memory bandwidth or computational resources, thereby reducing performance. This paper introduces a hardware performance analysis framework for identifying bottlenecks in the early stages of CNN hardware design. We demonstrate how the proposed method can help in evaluating different architecture alternatives of resource-restricted CNN accelerators (e.g., part of real-time embedded systems) early in design stages and, thus, prevent making design mistakes.