Iterative reconstruction in diffraction tomography using nonuniform fast Fourier transform. - Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky (ISBI, 2002)

Optimal nonlinear estimation of photon coordinates in PET. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ISBI, 2002)

Reconstruction in Diffraction Ultrasound Tomography Using Non-Uniform FFT. - Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky; Haim Azhari (IEEE Trans. Medical Imaging, 2002)

Expression-Invariant 3D Face Recognition. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (AVBPA, 2003)

Separation of semireflective layers using sparse ICA. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICASSP (3), 2003)

Separation of reflections via sparse ICA. - Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICIP (1), 2003)

Face Recognition from Facial Surface Metric. - Alexander M. Bronstein; Michael M. Bronstein; Alon Spira; Ron Kimmel (ECCV (2), 2004)

Blind Deconvolution Using the Relative Newton Method. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky (ICA, 2004)

Optimal Sparse Representations for Blind Deconvolution of Images. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICA, 2004)

QML Blind Deconvolution: Asymptotic Analysis. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICA, 2004)

Fusion of 2D and 3D data in three-dimensional face recognition. - Alexander M. Bronstein; Michael M. Bronstein; Eyal Gordon; Ron Kimmel (ICIP, 2004)

Optimal sparse representations for blind source separation and blind deconvolution: a learning approach. - Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICIP, 2004)

Fast relative newton algorithm for blind deconvolution of images. - Alexander M. Bronstein; Michael Zibulevsky; Michael M. Bronstein; Yehoshua Y. Zeevi (ICIP, 2004)

Quasi-Maximum Likelihood Blind Deconvolution of Images Acquired Through Scattering Media. - Michael M. Bronstein; Alexander M. Bronstein; Yehoshua Y. Zeevi; Michael Zibulevsky (ISBI, 2004)

Blind source separation using block-coordinate relative Newton method. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky (Signal Process., 2004)

Blind separation of tissues in multi-modal MRI using Sparse Component Analysis. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (EUSIPCO, 2005)

Expression-invariant face recognition via spherical embedding. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ICIP (3), 2005)

"Unmixing" tissues: sparse component analysis in multi-contrast MRI. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (ICIP (2), 2005)

Isometric Embedding of Facial Surfaces into. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Scale-Space, 2005)

Three-Dimensional Face Recognition. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Int. J. Comput. Vis., 2005)

Sparse ICA for blind separation of transmitted and reflected images. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (Int. J. Imaging Syst. Technol., 2005)

Blind deconvolution of images using optimal sparse representations. - Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi (IEEE Trans. Image Process., 2005)

Relative optimization for blind deconvolution. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky (IEEE Trans. Signal Process., 2005)

Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky (IEEE Trans. Signal Process., 2005)

Matching Two-Dimensional Articulated Shapes Using Generalized Multidimensional Scaling. - Alexander M. Bronstein; Michael M. Bronstein; Alfred M. Bruckstein; Ron Kimmel (AMDO, 2006)

Facetoface: An Isometric Model for Facial Animation. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (AMDO, 2006)

Topologically Constrained Isometric Embedding. - Guy Rosman; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Human Motion, 2006)

Robust Expression-Invariant Face Recognition from Partially Missing Data. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ECCV (3), 2006)

On Separation of Semitransparent Dynamic Images from Static Background. - Alexander M. Bronstein; Michael M. Bronstein; Michael Zibulevsky (ICA, 2006)

Multigrid multidimensional scaling. - Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel; Irad Yavneh (Numer. Linear Algebra Appl., 2006)

Efficient Computation of Isometry-Invariant Distances Between Surfaces. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (SIAM J. Sci. Comput., 2006)

Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ICCV, 2007)

Symmetries of non-rigid shapes. - Dan Raviv; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ICCV, 2007)

Paretian Similarity for Partial Comparison of Non-rigid Objects. - Alexander M. Bronstein; Michael M. Bronstein; Alfred M. Bruckstein; Ron Kimmel (SSVM, 2007)

Weighted distance maps computation on parametric three-dimensional manifolds. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (J. Comput. Phys., 2007)

Expression-Invariant Representations of Faces. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (IEEE Trans. Image Process., 2007)

Calculus of Nonrigid Surfaces for Geometry and Texture Manipulation. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (IEEE Trans. Vis. Comput. Graph., 2007)

Isometry-invariant surface matching: numerical algorithms and applications. - Michael M. Bronstein (2007)

Not only size matters: Regularized partial matching of nonrigid shapes. - Alexander M. Bronstein; Michael M. Bronstein (CVPR Workshops, 2008)

Regularized Partial Matching of Rigid Shapes. - Alexander M. Bronstein; Michael M. Bronstein (ECCV (2), 2008)

Analysis of Two-Dimensional Non-Rigid Shapes. - Alexander M. Bronstein; Michael M. Bronstein; Alfred M. Bruckstein; Ron Kimmel (Int. J. Comput. Vis., 2008)

Parallel algorithms for approximation of distance maps on parametric surfaces. - Ofir Weber; Yohai S. Devir; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ACM Trans. Graph., 2008)

On reconstruction of non-rigid shapes with intrinsic regularization. - Yohai S. Devir; Guy Rosman; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ICCV Workshops, 2009)

Shape Google: a computer vision approach to isometry invariant shape retrieval. - Maks Ovsjanikov; Alexander M. Bronstein; Michael M. Bronstein; Leonidas J. Guibas (ICCV Workshops, 2009)

3D-color video camera. - O. Rubinstein; Yaron Honen; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (ICCV Workshops, 2009)

Partial Similarity of Objects, or How to Compare a Centaur to a Horse. - Alexander M. Bronstein; Michael M. Bronstein; Alfred M. Bruckstein; Ron Kimmel (Int. J. Comput. Vis., 2009)

Topology-Invariant Similarity of Nonrigid Shapes. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Int. J. Comput. Vis., 2009)

Partial Similarity of Shapes Using a Statistical Significance Measure. - Alexander M. Bronstein; Michael M. Bronstein; Yair Carmon; Ron Kimmel (IPSJ Trans. Comput. Vis. Appl., 2009)

Numerical Geometry of Non-Rigid Shapes - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Springer, 2009)

SHREC'10 Track: Feature Detection and Description. - Alexander M. Bronstein; Michael M. Bronstein; Benjamin Bustos; Umberto Castellani; Marco Cristani; Bianca Falcidieno; Leonidas J. Guibas; Iasonas Kokkinos; Vittorio Murino; Maks Ovsjanikov; Giuseppe Patanè 001; Ivan Sipiran; et al. (3DOR@Eurographics, 2010)

SHREC'10 Track: Correspondence Finding. - Alexander M. Bronstein; Michael M. Bronstein; Umberto Castellani; Anastasia Dubrovina; Leonidas J. Guibas; Radu Horaud; Ron Kimmel; David Knossow; Etienne von Lavante; Diana Mateus; Maks Ovsjanikov; Avinash Sharma 001 (3DOR@Eurographics, 2010)

SHREC'10 Track: Robust Shape Retrieval. - Alexander M. Bronstein; Michael M. Bronstein; Umberto Castellani; Bianca Falcidieno; Andrea Fusiello; Afzal Godil; Leonidas J. Guibas; Iasonas Kokkinos; Zhouhui Lian; Maks Ovsjanikov; Giuseppe Patanè 001; Michela Spagnuolo; et al. (3DOR@Eurographics, 2010)

Data fusion through cross-modality metric learning using similarity-sensitive hashing. - Michael M. Bronstein; Alexander M. Bronstein; Fabrice Michel; Nikos Paragios (CVPR, 2010)

Scale-invariant heat kernel signatures for non-rigid shape recognition. - Michael M. Bronstein; Iasonas Kokkinos (CVPR, 2010)

Spatially-Sensitive Affine-Invariant Image Descriptors. - Alexander M. Bronstein; Michael M. Bronstein (ECCV (2), 2010)

Intrinsic Regularity Detection in 3D Geometry. - Niloy J. Mitra; Alexander M. Bronstein; Michael M. Bronstein (ECCV (3), 2010)

Volumetric heat kernel signatures. - Dan Raviv; Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel (3DOR@MM, 2010)

The Video Genome - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (CoRR, 2010)

A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching. - Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel; Mona Mahmoudi; Guillermo Sapiro (Int. J. Comput. Vis., 2010)

Full and Partial Symmetries of Non-rigid Shapes. - Dan Raviv; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (Int. J. Comput. Vis., 2010)

Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding. - Guy Rosman; Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel (Int. J. Comput. Vis., 2010)

SHREC '11: Robust Feature Detection and Description Benchmark. - Edmond Boyer; Alexander M. Bronstein; Michael M. Bronstein; Benjamin Bustos; Tal Darom; Radu Horaud; Ingrid Hotz; Yosi Keller; Johannes Keustermans; Artiom Kovnatsky; Roee Litman; Jan Reininghaus; et al. (3DOR@Eurographics, 2011)

Affine-invariant diffusion geometry for the analysis of deformable 3D shapes. - Dan Raviv; Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel; Nir A. Sochen (CVPR, 2011)

Boosted metric learning for 3D multi-modal deformable registration. - Fabrice Michel; Michael M. Bronstein; Alexander M. Bronstein; Nikos Paragios (ISBI, 2011)

Deformable Shape Retrieval by Learning Diffusion Kernels. - Yonathan Aflalo; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (SSVM, 2011)

Shape Palindromes: Analysis of Intrinsic Symmetries in 2D Articulated Shapes. - Amit Hooda; Michael M. Bronstein; Alexander M. Bronstein; Radu Horaud (SSVM, 2011)

Photometric Heat Kernel Signatures. - Artiom Kovnatsky; Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel (SSVM, 2011)

A Correspondence-Less Approach to Matching of Deformable Shapes. - Jonathan Pokrass; Alexander M. Bronstein; Michael M. Bronstein (SSVM, 2011)

Group-Valued Regularization Framework for Motion Segmentation of Dynamic Non-rigid Shapes. - Guy Rosman; Michael M. Bronstein; Alexander M. Bronstein; Alon Wolf; Ron Kimmel (SSVM, 2011)

Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching. - Chaohui Wang; Michael M. Bronstein; Alexander M. Bronstein; Nikos Paragios (SSVM, 2011)

Equi-affine Invariant Geometries of Articulated Objects. - Dan Raviv; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel; Nir A. Sochen (Theoretical Foundations of Computer Vision, 2011)

Diffusion-geometric maximally stable component detection in deformable shapes. - Roee Litman; Alexander M. Bronstein; Michael M. Bronstein (Comput. Graph., 2011)

Affine-invariant geodesic geometry of deformable 3D shapes. - Dan Raviv; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel; Nir A. Sochen (Comput. Graph., 2011)

Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis - Artiom Kovnatsky; Michael M. Bronstein; Alexander M. Bronstein; Ron Kimmel (CoRR, 2011)

SHREC 2011: robust feature detection and description benchmark - Edmond Boyer; Alexander M. Bronstein; Michael M. Bronstein; Benjamin Bustos; Tal Darom; Radu Horaud; Ingrid Hotz; Yosi Keller; Johannes Keustermans; Artiom Kovnatsky; Roee Litman; Jan Reininghaus; et al. (CoRR, 2011)

Kernel diff-hash - Michael M. Bronstein (CoRR, 2011)

Multimodal diff-hash - Michael M. Bronstein (CoRR, 2011)

Descriptor learning for omnidirectional image matching - Jonathan Masci; Davide Migliore; Michael M. Bronstein; Jürgen Schmidhuber (CoRR, 2011)

Shape Recognition with Spectral Distances. - Michael M. Bronstein; Alexander M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

Are MSER Features Really Interesting? - Ron Kimmel; Cuiping Zhang; Alexander M. Bronstein; Michael M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

Lazy Sliding Window Implementation of the Bilateral Filter on Parallel Architectures. - Michael M. Bronstein (IEEE Trans. Image Process., 2011)

Shape google: Geometric words and expressions for invariant shape retrieval. - Alexander M. Bronstein; Michael M. Bronstein; Leonidas J. Guibas; Maks Ovsjanikov (ACM Trans. Graph., 2011)

Feature-Based Methods in 3D Shape Analysis. - Alexander M. Bronstein; Michael M. Bronstein; Maks Ovsjanikov (3D Imaging, Analysis and Applications, 2012)

Structure from Motion Using Augmented Lagrangian Robust Factorization. - Klaus Glashoff; Michael M. Bronstein (3DIMPVT, 2012)

Affine-Invariant Photometric Heat Kernel Signatures. - Artiom Kovnatsky; Michael M. Bronstein; Alexander M. Bronstein; Dan Raviv; Ron Kimmel (3DOR@Eurographics, 2012)

Articulated Motion Segmentation of Point Clouds by Group-Valued Regularization. - Guy Rosman; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (3DOR@Eurographics, 2012)

Intrinsic shape context descriptors for deformable shapes. - Iasonas Kokkinos; Michael M. Bronstein; Roee Litman; Alexander M. Bronstein (CVPR, 2012)

Stable Spectral Mesh Filtering. - Artiom Kovnatsky; Michael M. Bronstein; Alexander M. Bronstein (ECCV Workshops (1), 2012)

Putting the Pieces Together: Regularized Multi-part Shape Matching. - Or Litany; Alexander M. Bronstein; Michael M. Bronstein (ECCV Workshops (1), 2012)

Group-Valued Regularization for Analysis of Articulated Motion. - Guy Rosman; Alexander M. Bronstein; Michael M. Bronstein; Xue-Cheng Tai; Ron Kimmel (ECCV Workshops (1), 2012)

Diffusion Geometry in Shape Analysis. - Michael M. Bronstein; Umberto Castellani; Alexander M. Bronstein (Eurographics (Tutorials), 2012)

Stable volumetric features in deformable shapes. - Roee Litman; Alexander M. Bronstein; Michael M. Bronstein (Comput. Graph., 2012)

Multimodal diffusion geometry by joint diagonalization of Laplacians - Davide Eynard; Klaus Glashoff; Michael M. Bronstein; Alexander M. Bronstein (CoRR, 2012)

Motion-based mesh segmentation using augmented silhouettes. - Stefano Marras; Michael M. Bronstein; Kai Hormann; Riccardo Scateni; Roberto Scopigno (Graph. Model., 2012)

LDAHash: Improved Matching with Smaller Descriptors. - Christoph Strecha; Alexander M. Bronstein; Michael M. Bronstein; Pascal Fua (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Stable Semi-local Features for Non-rigid Shapes. - Roee Litman; Alexander M. Bronstein; Michael M. Bronstein (Innovations for Shape Analysis, Models and Algorithms, 2013)

Group-Valued Regularization for Motion Segmentation of Articulated Shapes. - Guy Rosman; Michael M. Bronstein; Alexander M. Bronstein; Alon Wolf; Ron Kimmel (Innovations for Shape Analysis, Models and Algorithms, 2013)

Learnable low rank sparse models for speech denoising. - Pablo Sprechmann; Alexander M. Bronstein; Michael M. Bronstein; Guillermo Sapiro (ICASSP, 2013)

Coupled quasi-harmonic bases. - Artiom Kovnatsky; Michael M. Bronstein; Alexander M. Bronstein; Klaus Glashoff; Ron Kimmel (Comput. Graph. Forum, 2013)

Sparse Modeling of Intrinsic Correspondences. - Jonathan Pokrass; Alexander M. Bronstein; Michael M. Bronstein; Pablo Sprechmann; Guillermo Sapiro (Comput. Graph. Forum, 2013)

Heat kernel coupling for multiple graph analysis. - Michael M. Bronstein; Klaus Glashoff (CoRR, 2013)

Making Laplacians commute. - Michael M. Bronstein; Klaus Glashoff; Terry A. Loring (CoRR, 2013)

Structure-preserving color transformations using Laplacian commutativity. - Davide Eynard; Artiom Kovnatsky; Michael M. Bronstein (CoRR, 2013)

Almost-commuting matrices are almost jointly diagonalizable - Klaus Glashoff; Michael M. Bronstein (CoRR, 2013)

Asymptotic metrics on the space of matrices under the commutation relation - Klaus Glashoff; Michael M. Bronstein (CoRR, 2013)

Coulomb Shapes: Using Electrostatic Forces for Deformation-invariant Shape Representation. - Davide Boscaini; Ramunas Girdziusas; Michael M. Bronstein (3DOR@Eurographics, 2014)

Shape Retrieval of Non-Rigid 3D Human Models. - David Pickup; Xianfang Sun; Paul L. Rosin; Ralph R. Martin; Z. Cheng; Zhouhui Lian; Masaki Aono; Abdessamad Ben Hamza; Alexander M. Bronstein; Michael M. Bronstein; S. Bu; Umberto Castellani; et al. (3DOR@Eurographics, 2014)

Quantifying 3D Shape Similarity Using Maps: Recent Trends, Applications and Perspectives. - Silvia Biasotti; Andrea Cerri; Alexander M. Bronstein; Michael M. Bronstein (Eurographics (State of the Art Reports), 2014)

Gamut mapping with image Laplacian commutators. - Artiom Kovnatsky; Davide Eynard; Michael M. Bronstein (ICIP, 2014)

Laplacian colormaps: a framework for structure-preserving color transformations. - Davide Eynard; Artiom Kovnatsky; Michael M. Bronstein (Comput. Graph. Forum, 2014)

Supervised learning of bag-of-features shape descriptors using sparse coding. - Roee Litman; Alexander M. Bronstein; Michael M. Bronstein; Umberto Castellani (Comput. Graph. Forum, 2014)

Shape-from-intrinsic operator. - Davide Boscaini; Davide Eynard; Michael M. Bronstein (CoRR, 2014)

Matrix Completion on Graphs. - Vassilis Kalofolias; Xavier Bresson; Michael M. Bronstein; Pierre Vandergheynst (CoRR, 2014)

Sparse similarity-preserving hashing. - Jonathan Masci; Alexander M. Bronstein; Michael M. Bronstein; Pablo Sprechmann; Guillermo Sapiro (ICLR, 2014)

Equi-affine Invariant Geometry for Shape Analysis. - Dan Raviv; Alexander M. Bronstein; Michael M. Bronstein; Dan Waisman; Nir A. Sochen; Ron Kimmel (J. Math. Imaging Vis., 2014)

Multimodal Similarity-Preserving Hashing. - Jonathan Masci; Michael M. Bronstein; Alexander M. Bronstein; Jürgen Schmidhuber (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Descriptor Learning for Omnidirectional Image Matching. - Jonathan Masci; Davide Migliore; Michael M. Bronstein; Jürgen Schmidhuber (Registration and Recognition in Images and Videos, 2014)

Functional correspondence by matrix completion. - Artiom Kovnatsky; Michael M. Bronstein; Xavier Bresson; Pierre Vandergheynst (CVPR, 2015)

Contactless biometric hand geometry recognition using a low-cost 3D camera. - Jan Svoboda; Michael M. Bronstein; Martin Drahanský (ICB, 2015)

Robust Principal Component Analysis on Graphs. - Nauman Shahid; Vassilis Kalofolias; Xavier Bresson; Michael M. Bronstein; Pierre Vandergheynst (ICCV, 2015)

Geodesic Convolutional Neural Networks on Riemannian Manifolds. - Jonathan Masci; Davide Boscaini; Michael M. Bronstein; Pierre Vandergheynst (ICCV Workshops, 2015)

Shape-from-Operator: Recovering Shapes from Intrinsic Operators. - Davide Boscaini; Davide Eynard; Drosos Kourounis; Michael M. Bronstein (Comput. Graph. Forum, 2015)

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. - Davide Boscaini; Jonathan Masci; Simone Melzi; Michael M. Bronstein; Umberto Castellani; Pierre Vandergheynst (Comput. Graph. Forum, 2015)

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds. - Jonathan Masci; Davide Boscaini; Michael M. Bronstein; Pierre Vandergheynst (CoRR, 2015)

Multimodal Manifold Analysis by Simultaneous Diagonalization of Laplacians. - Davide Eynard; Artiom Kovnatsky; Michael M. Bronstein; Klaus Glashoff; Alexander M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Manifold Intrinsic Similarity. - Alexander M. Bronstein; Michael M. Bronstein (Handbook of Mathematical Methods in Imaging, 2015)

Sparse Models for Intrinsic Shape Correspondence. - Jonathan Pokrass; Alexander M. Bronstein; Michael M. Bronstein; Pablo Sprechmann; Guillermo Sapiro (Perspectives in Shape Analysis, 2016)

Matching Deformable Objects in Clutter. - Luca Cosmo; Emanuele Rodolà; Jonathan Masci; Andrea Torsello; Michael M. Bronstein (3DV, 2016)

Coupled Functional Maps. - Davide Eynard; Emanuele Rodolà; Klaus Glashoff; Michael M. Bronstein (3DV, 2016)

Shape Analysis with Anisotropic Windowed Fourier Transform. - Simone Melzi; Emanuele Rodolà; Umberto Castellani; Michael M. Bronstein (3DV, 2016)

Partial Matching of Deformable Shapes. - Luca Cosmo; Emanuele Rodolà; Michael M. Bronstein; Andrea Torsello; Daniel Cremers; Yusuf Sahillioglu (3DOR@Eurographics, 2016)

Matching of Deformable Shapes with Topological Noise. - Zorah Lähner; Emanuele Rodolà; Michael M. Bronstein; Daniel Cremers; Oliver Burghard; Luca Cosmo; Alexander Dieckmann; Reinhard Klein; Yusuf Sahillioglu (3DOR@Eurographics, 2016)

Efficient Globally Optimal 2D-to-3D Deformable Shape Matching. - Zorah Lähner; Emanuele Rodolà; Frank R. Schmidt; Michael M. Bronstein; Daniel Cremers (CVPR, 2016)

MADMM: A Generic Algorithm for Non-smooth Optimization on Manifolds. - Artiom Kovnatsky; Klaus Glashoff; Michael M. Bronstein (ECCV (5), 2016)

Deep Learning for Shape Analysis. - Michael M. Bronstein; Evangelos Kalogerakis; Emanuele Rodolà; Jonathan Masci; Davide Boscaini (Eurographics (Tutorials), 2016)

Palmprint recognition via discriminative index learning. - Jan Svoboda; Jonathan Masci; Michael M. Bronstein (ICPR, 2016)

Learning shape correspondence with anisotropic convolutional neural networks. - Davide Boscaini; Jonathan Masci; Emanuele Rodolà; Michael M. Bronstein (NIPS, 2016)

Geometric deep learning. - Jonathan Masci; Emanuele Rodolà; Davide Boscaini; Michael M. Bronstein; Hao Li 015 (SIGGRAPH ASIA Courses, 2016)

Computing and processing correspondences with functional maps. - Maks Ovsjanikov; Etienne Corman; Michael M. Bronstein; Emanuele Rodolà; Mirela Ben-Chen; Leonidas J. Guibas; Frédéric Chazal; Alexander M. Bronstein (SIGGRAPH ASIA Courses, 2016)

Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment. - Silvia Biasotti; Andrea Cerri; Alexander M. Bronstein; Michael M. Bronstein (Comput. Graph. Forum, 2016)

Anisotropic Diffusion Descriptors. - Davide Boscaini; Jonathan Masci; Emanuele Rodolà; Michael M. Bronstein; Daniel Cremers (Comput. Graph. Forum, 2016)

Non-Rigid Puzzles. - Or Litany; Emanuele Rodolà; Alexander M. Bronstein; Michael M. Bronstein; Daniel Cremers (Comput. Graph. Forum, 2016)

Shape Retrieval of Non-rigid 3D Human Models. - David Pickup; Xianfang Sun; Paul L. Rosin; Ralph R. Martin; Z. Cheng; Zhouhui Lian; Masaki Aono; A. Ben Hamza; Alexander M. Bronstein; Michael M. Bronstein; S. Bu; Umberto Castellani; et al. (Int. J. Comput. Vis., 2016)

Spatial Maps: From Low Rank Spectral to Sparse Spatial Functional Representations. - Andrea Gasparetto; Luca Cosmo; Emanuele Rodolà; Michael M. Bronstein; Andrea Torsello (3DV, 2017)

Efficient Deformable Shape Correspondence via Kernel Matching. - Matthias Vestner; Zorah Lähner; Amit Boyarski; Or Litany; Ron Slossberg; Tal Remez; Emanuele Rodolà; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel; Daniel Cremers (3DV, 2017)

Deformable Shape Retrieval with Missing Parts. - Emanuele Rodolà; Luca Cosmo; Or Litany; Michael M. Bronstein; Alexander M. Bronstein; Nicolas Audebert; A. Ben Hamza; Alexandre Boulch; Umberto Castellani; Minh N. Do; Duc Anh Duong; Takahiko Furuya; et al. (3DOR@Eurographics, 2017)

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. - Federico Monti; Davide Boscaini; Jonathan Masci; Emanuele Rodolà; Jan Svoboda; Michael M. Bronstein (CVPR, 2017)

Generative convolutional networks for latent fingerprint reconstruction. - Jan Svoboda; Federico Monti; Michael M. Bronstein (IJCB, 2017)

Deep Functional Maps: Structured Prediction for Dense Shape Correspondence. - Or Litany; Tal Remez; Emanuele Rodolà; Alexander M. Bronstein; Michael M. Bronstein (ICCV, 2017)

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. - Federico Monti; Michael M. Bronstein; Xavier Bresson (NIPS, 2017)

Subspace Least Squares Multidimensional Scaling. - Amit Boyarski; Alexander M. Bronstein; Michael M. Bronstein (SSVM, 2017)

Fully Spectral Partial Shape Matching. - Or Litany; Emanuele Rodolà; Alexander M. Bronstein; Michael M. Bronstein (Comput. Graph. Forum, 2017)

Partial Functional Correspondence. - Emanuele Rodolà; Luca Cosmo; Michael M. Bronstein; Andrea Torsello; Daniel Cremers (Comput. Graph. Forum, 2017)

Geometric Deep Learning: Going beyond Euclidean data. - Michael M. Bronstein; Joan Bruna; Yann LeCun; Arthur Szlam; Pierre Vandergheynst (IEEE Signal Process. Mag., 2017)

Deformable Shape Completion With Graph Convolutional Autoencoders. - Or Litany; Alexander M. Bronstein; Michael M. Bronstein; Ameesh Makadia (CVPR, 2018)

MOTIFNET: A Motif-Based Graph Convolutional Network for Directed Graphs. - Federico Monti; Karl Otness; Michael M. Bronstein (DSW, 2018)

Deep Geometric Matrix Completion: A New Way for Recommender Systems. - Federico Monti; Michael M. Bronstein; Xavier Bresson (ICASSP, 2018)

Graph Neural Networks for IceCube Signal Classification. - Nicholas Choma; Federico Monti; Lisa Gerhardt; Tomasz Palczewski; Zahra Ronaghi; Prabhat; Wahid Bhimji; Michael M. Bronstein; Spencer R. Klein; Joan Bruna (ICMLA, 2018)

Functional Maps on Product Manifolds. - Emanuele Rodolà; Zorah Lähner; Alex M. Bronstein; Michael M. Bronstein; Justin Solomon 001 (SGP (Posters), 2018)

Interactive Curve Constrained Functional Maps. - Anne Gehre; Michael M. Bronstein; Leif Kobbelt; Justin Solomon 001 (Comput. Graph. Forum, 2018)

Localized Manifold Harmonics for Spectral Shape Analysis. - Simone Melzi; Emanuele Rodolà; Umberto Castellani; Michael M. Bronstein (Comput. Graph. Forum, 2018)

Improved Functional Mappings via Product Preservation. - Dorian Nogneng; Simone Melzi; Emanuele Rodolà; Umberto Castellani; Michael M. Bronstein; Maks Ovsjanikov (Comput. Graph. Forum, 2018)

Kernel Functional Maps. - Larry Wang; Anne Gehre; Michael M. Bronstein; Justin Solomon 001 (Comput. Graph. Forum, 2018)

Dual-Primal Graph Convolutional Networks. - Federico Monti; Oleksandr Shchur; Aleksandar Bojchevski; Or Litany; Stephan Günnemann; Michael M. Bronstein (CoRR, 2018)

Shape Correspondence with Isometric and Non-Isometric Deformations. - Roberto M. Dyke; C. Stride; Yu-Kun Lai; Paul L. Rosin; Mathieu Aubry; Amit Boyarski; Alexander M. Bronstein; Michael M. Bronstein; Daniel Cremers; Matthew Fisher; Thibault Groueix; Daoliang Guo; et al. (3DOR@Eurographics, 2019)

Single Image 3D Hand Reconstruction with Mesh Convolutions. - Dominik Kulon; Haoyang Wang 002; Riza Alp Güler; Michael M. Bronstein; Stefanos Zafeiriou (BMVC, 2019)

Isospectralization, or How to Hear Shape, Style, and Correspondence. - Luca Cosmo; Mikhail Panine; Arianna Rampini; Maks Ovsjanikov; Michael M. Bronstein; Emanuele Rodolà (CVPR, 2019)

GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching. - Simone Melzi; Riccardo Spezialetti; Federico Tombari; Michael M. Bronstein; Luigi Di Stefano; Emanuele Rodolà (CVPR, 2019)

Deep Learning for Computer Graphics and Geometry Processing. - Michael M. Bronstein; Leonidas J. Guibas; Iasonas Kokkinos; Or Litany; Niloy J. Mitra; Federico Monti; Emanuele Rodolà (Eurographics (Tutorials), 2019)

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation. - Giorgos Bouritsas; Sergiy Bokhnyak; Stylianos Ploumpis; Stefanos Zafeiriou; Michael M. Bronstein (ICCV, 2019)

SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator. - Shunwang Gong; Lei Chen 062; Michael M. Bronstein; Stefanos Zafeiriou (ICCV Workshops, 2019)

PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. - Jan Svoboda; Jonathan Masci; Federico Monti; Michael M. Bronstein; Leonidas J. Guibas (ICLR (Poster), 2019)

Functional Maps Representation On Product Manifolds. - Emanuele Rodolà; Zorah Lähner; Alexander M. Bronstein; Michael M. Bronstein; Justin Solomon 001 (Comput. Graph. Forum, 2019)

Fake News Detection on Social Media using Geometric Deep Learning. - Federico Monti; Fabrizio Frasca; Davide Eynard; Damon Mannion; Michael M. Bronstein (CoRR, 2019)

MeshGAN: Non-linear 3D Morphable Models of Faces. - Shiyang Cheng 001; Michael M. Bronstein; Yuxiang Zhou; Irene Kotsia; Maja Pantic; Stefanos Zafeiriou (CoRR, 2019)

ncRNA Classification with Graph Convolutional Networks. - Emanuele Rossi; Federico Monti; Michael M. Bronstein; Pietro Liò (CoRR, 2019)

Transferability of Spectral Graph Convolutional Neural Networks. - Ron Levie; Michael M. Bronstein; Gitta Kutyniok (CoRR, 2019)

Learning interpretable disease self-representations for drug repositioning. - Fabrizio Frasca; Diego Galeano; Guadalupe Gonzalez; Ivan Laponogov; Kirill A. Veselkov; Alberto Paccanaro; Michael M. Bronstein (CoRR, 2019)

Dynamic Graph CNN for Learning on Point Clouds. - Yue Wang 041; Yongbin Sun; Ziwei Liu 002; Sanjay E. Sarma; Michael M. Bronstein; Justin M. Solomon (ACM Trans. Graph., 2019)

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters. - Ron Levie; Federico Monti; Xavier Bresson; Michael M. Bronstein (IEEE Trans. Signal Process., 2019)

Geometrically Principled Connections in Graph Neural Networks. - Shunwang Gong; Mehdi Bahri; Michael M. Bronstein; Stefanos Zafeiriou (CVPR, 2020)

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild. - Dominik Kulon; Riza Alp Güler; Iasonas Kokkinos; Michael M. Bronstein; Stefanos Zafeiriou (CVPR, 2020)

The Average Mixing Kernel Signature. - Luca Cosmo; Giorgia Minello; Michael M. Bronstein; Luca Rossi 004; Andrea Torsello (ECCV (20), 2020)

Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition. - Jan Svoboda; Pietro Astolfi; Davide Boscaini; Jonathan Masci; Michael M. Bronstein (IJCB, 2020)

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties. - Soha Sadat Mahdi; Nele Nauwelaers; Philip Joris; Giorgos Bouritsas; Shunwang Gong; Sergiy Bokhnyak; Susan Walsh; Mark D. Shriver; Michael M. Bronstein; Peter Claes (ICPR, 2020)

Latent-Graph Learning for Disease Prediction. - Luca Cosmo; Anees Kazi; Seyed-Ahmad Ahmadi; Nassir Navab; Michael M. Bronstein (MICCAI (2), 2020)

Fast geometric learning with symbolic matrices. - Jean Feydy; Joan Alexis Glaunès; Benjamin Charlier; Michael M. Bronstein (NeurIPS, 2020)

RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home Timeline. - Vito Walter Anelli; Amra Delic; Gabriele Sottocornola; Jessie Smith; Nazareno Andrade; Luca Belli; Michael M. Bronstein; Akshay Gupta; Sofia Ira Ktena; Alexandre Lung-Yut-Fong; Frank Portman; Alykhan Tejani; et al. (RecSys, 2020)

Tuning Word2vec for Large Scale Recommendation Systems. - Benjamin Paul Chamberlain; Emanuele Rossi; Dan Shiebler; Suvash Sedhain; Michael M. Bronstein (RecSys, 2020)

Graph Attentional Autoencoder for Anticancer Hyperfood Prediction. - Guadalupe Gonzalez; Shunwang Gong; Ivan Laponogov; Kirill A. Veselkov; Michael M. Bronstein (CoRR, 2020)

Differentiable Graph Module (DGM) Graph Convolutional Networks. - Anees Kazi; Luca Cosmo; Nassir Navab; Michael M. Bronstein (CoRR, 2020)

Shape retrieval of non-rigid 3d human models. - David Pickup; Xianfang Sun; Paul L. Rosin; Ralph R. Martin; Z. Cheng; Zhouhui Lian; Masaki Aono; A. Ben Hamza; Alexander M. Bronstein; Michael M. Bronstein; S. Bu; Umberto Castellani; et al. (CoRR, 2020)

Latent Patient Network Learning for Automatic Diagnosis. - Luca Cosmo; Anees Kazi; Seyed-Ahmad Ahmadi; Nassir Navab; Michael M. Bronstein (CoRR, 2020)

SIGN: Scalable Inception Graph Neural Networks. - Emanuele Rossi; Fabrizio Frasca; Ben Chamberlain 001; Davide Eynard; Michael M. Bronstein; Federico Monti (CoRR, 2020)

Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline. - Luca Belli; Sofia Ira Ktena; Alykhan Tejani; Alexandre Lung-Yut-Fong; Frank Portman; Xiao Zhu; Yuanpu Xie; Akshay Gupta; Michael M. Bronstein; Amra Delic; Gabriele Sottocornola; Vito Walter Anelli; et al. (CoRR, 2020)

Temporal Graph Networks for Deep Learning on Dynamic Graphs. - Emanuele Rossi; Ben Chamberlain 001; Fabrizio Frasca; Davide Eynard; Federico Monti; Michael M. Bronstein (CoRR, 2020)

Utilising Graph Machine Learning within Drug Discovery and Development. - Thomas Gaudelet; Ben Day; Arian R. Jamasb; Jyothish Soman; Cristian Regep; Gertrude Liu; Jeremy B. R. Hayter; Richard Vickers; Charles Roberts; Jian Tang 005; David Roblin; Tom L. Blundell; et al. (CoRR, 2020)

Intel® RealSense™ SR300 Coded Light Depth Camera. - Aviad Zabatani; Vitaly Surazhsky; Erez Sperling; Sagi Ben-Moshe; Ohad Menashe; David H. Silver; Zachi Karni; Alexander M. Bronstein; Michael M. Bronstein; Ron Kimmel (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Graph Signal Processing for Machine Learning: A Review and New Perspectives. - Xiaowen Dong 001; Dorina Thanou; Laura Toni; Michael M. Bronstein; Pascal Frossard (IEEE Signal Process. Mag., 2020)

Fast End-to-End Learning on Protein Surfaces. - Freyr Sverrisson; Jean Feydy; Bruno E. Correia; Michael M. Bronstein (CVPR, 2021)

GRAND: Graph Neural Diffusion. - Ben Chamberlain 001; James Rowbottom; Maria I. Gorinova 001; Michael M. Bronstein; Stefan Webb; Emanuele Rossi (ICML, 2021)

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. - Cristian Bodnar; Fabrizio Frasca; Yuguang Wang 001; Nina Otter; Guido F. Montúfar; Pietro Lió; Michael M. Bronstein (ICML, 2021)

Unsupervised Diffeomorphic Surface Registration and Non-linear Modelling. - Balder Croquet; Daan Christiaens; Seth M. Weinberg; Michael M. Bronstein; Dirk Vandermeulen; Peter Claes (MICCAI (4), 2021)

Weisfeiler and Lehman Go Cellular: CW Networks. - Cristian Bodnar; Fabrizio Frasca; Nina Otter; Yuguang Wang 001; Pietro Liò; Guido F. Montúfar; Michael M. Bronstein (NeurIPS, 2021)

Partition and Code: learning how to compress graphs. - Giorgos Bouritsas; Andreas Loukas; Nikolaos Karalias; Michael M. Bronstein (NeurIPS, 2021)

Beltrami Flow and Neural Diffusion on Graphs. - Ben Chamberlain 001; James Rowbottom; Davide Eynard; Francesco Di Giovanni; Xiaowen Dong 001; Michael M. Bronstein (NeurIPS, 2021)

RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter's Home Timeline. - Vito Walter Anelli; Saikishore Kalloori; Bruce Ferwerda; Luca Belli; Alykhan Tejani; Frank Portman; Alexandre Lung-Yut-Fong; Ben Chamberlain 001; Yuanpu Xie; Jonathan Hunt; Michael M. Bronstein; Wenzhe Shi (RecSys, 2021)

The 2021 RecSys Challenge Dataset: Fairness is not optional. - Luca Belli; Alykhan Tejani; Frank Portman; Alexandre Lung-Yut-Fong; Ben Chamberlain 001; Yuanpu Xie; Kristian Lum; Jonathan Hunt; Michael M. Bronstein; Vito Walter Anelli; Saikishore Kalloori; Bruce Ferwerda; et al. (RecSys Challenge, 2021)

GReS: Workshop on Graph Neural Networks for Recommendation and Search. - Thibaut Thonet; Stéphane Clinchant; Carlos Lassance; Elvin Isufi; Jiaqi Ma 001; Yutong Xie 007; Jean-Michel Renders; Michael M. Bronstein (RecSys, 2021)

Utilizing graph machine learning within drug discovery and development. - Thomas Gaudelet; Ben Day; Arian R. Jamasb; Jyothish Soman; Cristian Regep; Gertrude Liu; Jeremy B. R. Hayter; Richard Vickers; Charles Roberts; Jian Tang 005; David Roblin; Tom L. Blundell; et al. (Briefings Bioinform., 2021)

Orthogonalized Fourier Polynomials for Signal Approximation and Transfer. - Filippo Maggioli; Simone Melzi; Maksim Ovsjanikov; Michael M. Bronstein; Emanuele Rodolà (Comput. Graph. Forum, 2021)

Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales. - Jacob Andreas; Gasper Begus; Michael M. Bronstein; Roee Diamant; Denley Delaney; Shane Gero; Shafi Goldwasser; David F. Gruber; Sarah de Haas; Peter Malkin; Roger Payne; Giovanni Petri; et al. (CoRR, 2021)

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. - Michael M. Bronstein; Joan Bruna; Taco Cohen; Petar Velickovic (CoRR, 2021)

Graph Kernel Neural Networks. - Luca Cosmo; Giorgia Minello; Michael M. Bronstein; Emanuele Rodolà; Luca Rossi 004; Andrea Torsello (CoRR, 2021)

Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation. - Mehdi Bahri; Eimear O' Sullivan; Shunwang Gong; Feng Liu 037; Xiaoming Liu 002; Michael M. Bronstein; Stefanos Zafeiriou (Int. J. Comput. Vis., 2021)

Transferability of Spectral Graph Convolutional Neural Networks. - Ron Levie; Wei Huang; Lorenzo Bucci; Michael M. Bronstein; Gitta Kutyniok (J. Mach. Learn. Res., 2021)

Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit. - Stefan C. Schonsheck; Michael M. Bronstein; Rongjie Lai (J. Sci. Comput., 2021)

Inverse Computational Spectral Geometry. - Emanuele Rodolà; Luca Cosmo; Maks Ovsjanikov; Arianna Rampini; Simone Melzi; Michael M. Bronstein; Riccardo Marin (Eurographics (Tutorials), 2022)

Equivariant Subgraph Aggregation Networks. - Beatrice Bevilacqua; Fabrizio Frasca; Derek Lim; Balasubramaniam Srinivasan; Chen Cai; Gopinath Balamurugan; Michael M. Bronstein; Haggai Maron (ICLR, 2022)

Understanding over-squashing and bottlenecks on graphs via curvature. - Jake Topping; Francesco Di Giovanni; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2022)

Learning to Infer Structures of Network Games. - Emanuele Rossi; Federico Monti; Yan Leng; Michael M. Bronstein; Xiaowen Dong 001 (ICML, 2022)

Graph-Coupled Oscillator Networks. - T. Konstantin Rusch; Ben Chamberlain 001; James Rowbottom; Siddhartha Mishra; Michael M. Bronstein (ICML, 2022)

Neural Diffusion PDEs, Differential Geometry, and Graph Neural Networks. - Michael M. Bronstein (IMPROVE, 2022)

Graph-based Representation Learning for Web-scale Recommender Systems. - Ahmed El-Kishky; Michael M. Bronstein; Ying Xiao; Aria Haghighi (KDD, 2022)

On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. - Emanuele Rossi; Henry Kenlay; Maria I. Gorinova 001; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (LoG, 2022)

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. - Cristian Bodnar; Francesco Di Giovanni; Benjamin Paul Chamberlain; Pietro Lió; Michael M. Bronstein (NeurIPS, 2022)

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries. - Fabrizio Frasca; Beatrice Bevilacqua; Michael M. Bronstein; Haggai Maron (NeurIPS, 2022)

Sheaf Neural Networks with Connection Laplacians. - Federico Barbero; Cristian Bodnar; Haitz Sáez de Ocáriz Borde; Michael M. Bronstein; Petar Velickovic; Pietro Liò (TAG-ML, 2022)

Multi-Scale Part-Based Syndrome Classification of 3D Facial Images. - Soha Sadat Mahdi; Harold S. Matthews; Nele Nauwelaers; Michiel Vanneste; Shunwang Gong; Giorgos Bouritsas; Gareth S. Baynam; Peter Hammond; Richard A. Spritz; Ophir D. Klein; Benedikt Hallgrímsson; Hilde Peeters; et al. (IEEE Access, 2022)

Heterogeneous manifolds for curvature-aware graph embedding. - Francesco Di Giovanni; Giulia Luise; Michael M. Bronstein (CoRR, 2022)

RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro. - Paul Bertin; Jarrid Rector-Brooks; Deepak Sharma; Thomas Gaudelet; Andrew Anighoro; Torsten Gross; Francisco Martinez-Pena; Eileen L. Tang; Suraj M. S; Cristian Regep; Jeremy B. R. Hayter; Maksym Korablyov; et al. (CoRR, 2022)

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. - Kamilia Mullakaeva; Luca Cosmo; Anees Kazi; Seyed-Ahmad Ahmadi; Nassir Navab; Michael M. Bronstein (CoRR, 2022)

Graph Anisotropic Diffusion. - Ahmed A. A. Elhag; Gabriele Corso; Hannes Stärk; Michael M. Bronstein (CoRR, 2022)

Graph Neural Networks as Gradient Flows. - Francesco Di Giovanni; James Rowbottom; Benjamin Paul Chamberlain; Thomas Markovich; Michael M. Bronstein (CoRR, 2022)

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design. - Ilia Igashov; Hannes Stärk; Clément Vignac; Victor Garcia Satorras; Pascal Frossard; Max Welling; Michael M. Bronstein; Bruno E. Correia (CoRR, 2022)

Structure-based Drug Design with Equivariant Diffusion Models. - Arne Schneuing; Yuanqi Du; Charles Harris; Arian R. Jamasb; Ilia Igashov; Weitao Du; Tom L. Blundell; Pietro Lió; Carla Gomes 001; Max Welling; Michael M. Bronstein; Bruno E. Correia (CoRR, 2022)

3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature. - Luca Cosmo; Giorgia Minello; Michael M. Bronstein; Emanuele Rodolà; Luca Rossi 004; Andrea Torsello (Int. J. Comput. Vis., 2022)

Guest Editorial: Non-Euclidean Machine Learning. - Stefanos Zafeiriou; Michael M. Bronstein; Taco Cohen; Oriol Vinyals; Le Song; Jure Leskovec; Pietro Liò; Joan Bruna; Marco Gori (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach. - Soha Sadat Mahdi; Nele Nauwelaers; Philip Joris; Giorgos Bouritsas; Shunwang Gong; Susan Walsh; Mark D. Shriver; Michael M. Bronstein; Peter Claes (IEEE Trans. Biom. Behav. Identity Sci., 2022)

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. - Mirco Mutti; Riccardo De Santi; Emanuele Rossi; Juan Felipe Calderón; Michael M. Bronstein; Marcello Restelli (AAAI, 2023)

Hyperbolic Deep Reinforcement Learning. - Edoardo Cetin; Benjamin Paul Chamberlain; Michael M. Bronstein; Jonathan J. Hunt (ICLR, 2023)

Graph Neural Networks for Link Prediction with Subgraph Sketching. - Benjamin Paul Chamberlain; Sergey Shirobokov; Emanuele Rossi; Fabrizio Frasca; Thomas Markovich; Nils Yannick Hammerla; Michael M. Bronstein; Max Hansmire (ICLR, 2023)

Gradient Gating for Deep Multi-Rate Learning on Graphs. - T. Konstantin Rusch; Benjamin Paul Chamberlain; Michael W. Mahoney; Michael M. Bronstein; Siddhartha Mishra (ICLR, 2023)

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. - Francesco Di Giovanni; Lorenzo Giusti; Federico Barbero; Giulia Luise; Pietro Lio; Michael M. Bronstein (ICML, 2023)

DRew: Dynamically Rewired Message Passing with Delay. - Benjamin Gutteridge; Xiaowen Dong 001; Michael M. Bronstein; Francesco Di Giovanni (ICML, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Temporal Graph Benchmark for Machine Learning on Temporal Graphs. - Shenyang Huang; Farimah Poursafaei; Jacob Danovitch; Matthias Fey; Weihua Hu; Emanuele Rossi; Jure Leskovec; Michael M. Bronstein; Guillaume Rabusseau; Reihaneh Rabbany (NeurIPS, 2023)

Curvature Filtrations for Graph Generative Model Evaluation. - Joshua Southern; Jeremy Wayland; Michael M. Bronstein; Bastian Rieck (NeurIPS, 2023)

Can strong structural encoding reduce the importance of Message Passing? - Floor Eijkelboom; Erik J. Bekkers; Michael M. Bronstein; Francesco Di Giovanni (TAG-ML, 2023)

A Survey on Oversmoothing in Graph Neural Networks. - T. Konstantin Rusch; Michael M. Bronstein; Siddhartha Mishra (CoRR, 2023)

On the Impact of Sample Size in Reconstructing Graph Signals. - Baskaran Sripathmanathan; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2023)

Social learning across symbolic cultural barriers in non-human cultures. - António Leitão; Maxime Lucas; Simone Poetto; Taylor A. Hersh; Shane Gero; David F. Gruber; Michael M. Bronstein; Giovanni Petri (CoRR, 2023)

GraphText: Graph Reasoning in Text Space. - Jianan Zhao 002; Le Zhuo; Yikang Shen; Meng Qu; Kai Liu; Michael M. Bronstein; Zhaocheng Zhu; Jian Tang 005 (CoRR, 2023)

Advective Diffusion Transformers for Topological Generalization in Graph Learning. - Qitian Wu; Chenxiao Yang; Kaipeng Zeng; Fan Nie; Michael M. Bronstein; Junchi Yan (CoRR, 2023)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. - Alexandre Duval; Simon V. Mathis; Chaitanya K. Joshi; Victor Schmidt; Santiago Miret; Fragkiskos D. Malliaros; Taco Cohen; Pietro Lio; Yoshua Bengio; Michael M. Bronstein (CoRR, 2023)

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. - Giorgos Bouritsas; Fabrizio Frasca; Stefanos Zafeiriou; Michael M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Differentiable Graph Module (DGM) for Graph Convolutional Networks. - Anees Kazi; Luca Cosmo; Seyed-Ahmad Ahmadi; Nassir Navab; Michael M. Bronstein (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Understanding convolution on graphs via energies. - Francesco Di Giovanni; James Rowbottom; Benjamin Paul Chamberlain; Thomas Markovich; Michael M. Bronstein (Trans. Mach. Learn. Res., 2023)

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. - Kamilia Zaripova; Luca Cosmo; Anees Kazi; Seyed-Ahmad Ahmadi; Michael M. Bronstein; Nassir Navab (Medical Image Anal., 2023)

Locality-Aware Graph Rewiring in GNNs. - Federico Barbero; Ameya Velingker; Amin Saberi; Michael M. Bronstein; Francesco Di Giovanni (ICLR, 2024)

From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module. - Claudio Battiloro; Indro Spinelli; Lev Telyatnikov; Michael M. Bronstein; Simone Scardapane; Paolo Di Lorenzo (ICLR, 2024)

SE(3)-Stochastic Flow Matching for Protein Backbone Generation. - Avishek Joey Bose; Tara Akhound-Sadegh; Guillaume Huguet; Kilian Fatras; Jarrid Rector-Brooks; Cheng-Hao Liu; Andrei Cristian Nica; Maksym Korablyov; Michael M. Bronstein; Alexander Tong 001 (ICLR, 2024)

RetroBridge: Modeling Retrosynthesis with Markov Bridges. - Ilia Igashov; Arne Schneuing; Marwin H. S. Segler; Michael M. Bronstein; Bruno E. Correia (ICLR, 2024)

Position: Future Directions in the Theory of Graph Machine Learning. - Christopher Morris 001; Fabrizio Frasca; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (ICML, 2024)

Cooperative Graph Neural Networks. - Ben Finkelshtein; Xingyue Huang; Michael M. Bronstein; Ismail Ilkan Ceylan (ICML, 2024)

Homomorphism Counts for Graph Neural Networks: All About That Basis. - Emily Jin; Michael M. Bronstein; Ismail Ilkan Ceylan; Matthias Lanzinger (ICML, 2024)

Position: Topological Deep Learning is the New Frontier for Relational Learning. - Theodore Papamarkou; Tolga Birdal; Michael M. Bronstein; Gunnar E. Carlsson; Justin Curry; Yue Gao; Mustafa Hajij; Roland Kwitt; Pietro Lio; Paolo Di Lorenzo; Vasileios Maroulas; Nina Miolane; et al. (ICML, 2024)

Fisher Flow Matching for Generative Modeling over Discrete Data. - Oscar Davis; Samuel Kessler; Mircea Petrache; Ismail Ilkan Ceylan; Michael M. Bronstein; Avishek Joey Bose (NeurIPS, 2024)

Learning on Large Graphs using Intersecting Communities. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (NeurIPS, 2024)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (NeurIPS, 2024)

Metric Flow Matching for Smooth Interpolations on the Data Manifold. - Kacper Kapusniak; Peter Potaptchik; Teodora Reu; Leo Zhang; Alexander Tong 001; Michael M. Bronstein; Avishek Joey Bose; Francesco Di Giovanni (NeurIPS, 2024)

On the Limitations of Fractal Dimension as a Measure of Generalization. - Charlie Tan; Inés García-Redondo; Qiquan Wang; Michael M. Bronstein; Anthea Monod (NeurIPS, 2024)

To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models. - Teodora Reu; Francisco Vargas 001; Anna Kerekes; Michael M. Bronstein (UAI, 2024)

The 1st International Workshop on Graph Foundation Models (GFM). - Haitao Mao; Jianan Zhao 002; Xiaoxin He; Zhikai Chen; Qian Huang; Zhaocheng Zhu; Jian Tang 005; Michael M. Bronstein; Xavier Bresson; Bryan Hooi; Haiyang Zhang; Xianfeng Tang; et al. (WWW (Companion Volume), 2024)

Future Directions in Foundations of Graph Machine Learning. - Christopher Morris 001; Nadav Dym; Haggai Maron; Ismail Ilkan Ceylan; Fabrizio Frasca; Ron Levie; Derek Lim; Michael M. Bronstein; Martin Grohe; Stefanie Jegelka (CoRR, 2024)

Revealing Decurve Flows for Generalized Graph Propagation. - Chen Lin 003; Liheng Ma; Yiyang Chen; Wanli Ouyang; Michael M. Bronstein; Philip H. S. Torr (CoRR, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - Theodore Papamarkou; Tolga Birdal; Michael M. Bronstein; Gunnar E. Carlsson; Justin Curry; Yue Gao; Mustafa Hajij; Roland Kwitt; Pietro Liò; Paolo Di Lorenzo; Vasileios Maroulas; Nina Miolane; et al. (CoRR, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity. - Joshua Southern; Francesco Di Giovanni; Michael M. Bronstein; Johannes F. Lutzeyer (CoRR, 2024)

Advancing Graph Convolutional Networks via General Spectral Wavelets. - Nian Liu; Xiaoxin He; Thomas Laurent 001; Francesco Di Giovanni; Michael M. Bronstein; Xavier Bresson (CoRR, 2024)

Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks. - T. Konstantin Rusch; Nathan Kirk; Michael M. Bronstein; Christiane Lemieux; Daniela Rus (CoRR, 2024)

Bundle Neural Networks for message diffusion on graphs. - Jacob Bamberger; Federico Barbero; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2024)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (CoRR, 2024)

GraphAny: A Foundation Model for Node Classification on Any Graph. - Jianan Zhao 002; Hesham Mostafa; Mikhail Galkin 001; Michael M. Bronstein; Zhaocheng Zhu; Jian Tang 005 (CoRR, 2024)

On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation. - Baskaran Sripathmanathan; Xiaowen Dong 001; Michael M. Bronstein (CoRR, 2024)

Topological Blind Spots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. - Yam Eitan; Yoav Gelberg; Guy Bar-Shalom; Fabrizio Frasca; Michael M. Bronstein; Haggai Maron (CoRR, 2024)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Zachary Quinn; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Yoshua Bengio; Pranam Chatterjee; Alexander Tong 001; Avishek Joey Bose (CoRR, 2024)

Relaxed Equivariance via Multitask Learning. - Ahmed A. A. Elhag; T. Konstantin Rusch; Francesco Di Giovanni; Michael M. Bronstein (CoRR, 2024)

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning. - Haitz Sáez de Ocáriz Borde; Artem Lukoianov; Anastasis Kratsios; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2024)

Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification. - Benedict Aaron Tjandra; Federico Barbero; Michael M. Bronstein (CoRR, 2024)

OpenQDC: Open Quantum Data Commons. - Cristian Gabellini; Nikhil Shenoy; Stephan Thaler; Semih Cantürk; Daniel McNeela; Dominique Beaini; Michael M. Bronstein; Prudencio Tossou (CoRR, 2024)

Equivariant 3D-conditional diffusion model for molecular linker design. - Ilia Igashov; Hannes Stärk; Clément Vignac; Arne Schneuing; Victor Garcia Satorras; Pascal Frossard; Max Welling; Michael M. Bronstein; Bruno E. Correia (Nat. Mac. Intell., 2024)

How does over-squashing affect the power of GNNs? - Francesco Di Giovanni; T. Konstantin Rusch; Michael M. Bronstein; Andreea Deac; Marc Lackenby; Siddhartha Mishra; Petar Velickovic (Trans. Mach. Learn. Res., 2024)

Structure-based drug design with equivariant diffusion models. - Arne Schneuing; Charles Harris; Yuanqi Du; Kieran Didi; Arian Rokkum Jamasb; Ilia Igashov; Weitao Du; Carla P. Gomes; Tom L. Blundell; Pietro Lio; Max Welling; Michael M. Bronstein; et al. (Nat. Comput. Sci., 2024)

Fully-inductive Node Classification on Arbitrary Graphs. - Jianan Zhao 002; Zhaocheng Zhu; Mikhail Galkin 001; Hesham Mostafa; Michael M. Bronstein; Jian Tang 005 (ICLR, 2025)

Bundle Neural Network for message diffusion on graphs. - Jacob Bamberger; Federico Barbero; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Homomorphism Counts as Structural Encodings for Graph Learning. - Linus Bao; Emily Jin; Michael M. Bronstein; Ismail Ilkan Ceylan; Matthias Lanzinger (ICLR, 2025)

Neural Spacetimes for DAG Representation Learning. - Haitz Sáez de Ocáriz Borde; Anastasis Kratsios; Marc T. Law; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 2025)

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity. - Yam Eitan; Yoav Gelberg; Guy Bar-Shalom; Fabrizio Frasca; Michael M. Bronstein; Haggai Maron (ICLR, 2025)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Pranam Chatterjee; Alexander Tong 001; Joey Bose (ICLR, 2025)

Multi-domain Distribution Learning for De Novo Drug Design. - Arne Schneuing; Ilia Igashov; Adrian W. Dobbelstein; Thomas Castiglione; Michael M. Bronstein; Bruno Correia (ICLR, 2025)

Understanding Virtual Nodes: Oversquashing and Node Heterogeneity. - Joshua Southern; Francesco Di Giovanni; Michael M. Bronstein; Johannes F. Lutzeyer (ICLR, 2025)

A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder. - Soha Sadat Mahdi; Eduarda Caldeira; Harold S. Matthews; Michiel Vanneste; Nele Nauwelaers; Meng Yuan; Giorgos Bouritsas; Gareth S. Baynam; Peter Hammond; Richard A. Spritz; Ophir D. Klein; Michael M. Bronstein; et al. (IEEE Access, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models. - Zifeng Ding; Yifeng Li; Yuan He 008; Antonio Norelli; Jingcheng Wu; Volker Tresp; Michael M. Bronstein; Yunpu Ma (Trans. Mach. Learn. Res., 2025)

Setting the Record Straight on Transformer Oversmoothing. - Gbètondji J.-S. Dovonon; Michael M. Bronstein; Matt J. Kusner (Trans. Mach. Learn. Res., 2025)

Link Prediction with Relational Hypergraphs. - Xingyue Huang; Miguel A. Romero Orth; Pablo Barceló; Michael M. Bronstein; Ismail Ilkan Ceylan (Trans. Mach. Learn. Res., 2025)

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. - Joshua Southern; Yam Eitan; Guy Bar-Shalom; Michael M. Bronstein; Haggai Maron; Fabrizio Frasca (CoRR, 2025)

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning. - Alvaro Arroyo; Alessio Gravina; Benjamin Gutteridge; Federico Barbero; Claudio Gallicchio; Xiaowen Dong 001; Michael M. Bronstein; Pierre Vandergheynst (CoRR, 2025)

How Expressive are Knowledge Graph Foundation Models? - Xingyue Huang; Pablo Barceló; Michael M. Bronstein; Ismail Ilkan Ceylan; Mikhail Galkin 001; Juan L. Reutter; Miguel Romero 001 (CoRR, 2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

Scalable Equilibrium Sampling with Sequential Boltzmann Generators. - Charlie B. Tan; Avishek Joey Bose; Chen Lin 003; Leon Klein; Michael M. Bronstein; Alexander Tong 001 (CoRR, 2025)

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement. - Huidong Liang; Haitz Sáez de Ocáriz Borde; Baskaran Sripathmanathan; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2025)

Why do LLMs attend to the first token? - Federico Barbero; Alvaro Arroyo; Xiangming Gu; Christos Perivolaropoulos; Michael M. Bronstein; Petar Velickovic; Razvan Pascanu (CoRR, 2025)

Efficient Learning on Large Graphs using a Densifying Regularity Lemma. - Jonathan Kouchly; Ben Finkelshtein; Michael M. Bronstein; Ron Levie (CoRR, 2025)

Graph Kernel Neural Networks. - Luca Cosmo; Giorgia Minello; Alessandro Bicciato; Michael M. Bronstein; Emanuele Rodolà; Luca Rossi 004; Andrea Torsello (IEEE Trans. Neural Networks Learn. Syst., 2025)

FORT: Forward-Only Regression Training of Normalizing Flows. - Danyal Rehman; Oscar Davis; Jiarui Lu; Jian Tang 005; Michael M. Bronstein; Yoshua Bengio; Alexander Tong 001; Avishek Joey Bose (CoRR, 2025)

Are Large Language Models Good Temporal Graph Learners? - Shenyang Huang; Ali Parviz; Emma Kondrup; Zachary Yang; Zifeng Ding; Michael M. Bronstein; Reihaneh Rabbany; Guillaume Rabusseau (CoRR, 2025)

On Measuring Long-Range Interactions in Graph Neural Networks. - Jacob Bamberger; Benjamin Gutteridge; Scott le Roux; Michael M. Bronstein; Xiaowen Dong 001 (CoRR, 2025)

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs. - Xingyue Huang; Mikhail Galkin 001; Michael M. Bronstein; Ismail Ilkan Ceylan (CoRR, 2025)

Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models. - Ben Finkelshtein; Ismail Ilkan Ceylan; Michael M. Bronstein; Ron Levie (CoRR, 2025)

Over-squashing in Spatiotemporal Graph Neural Networks. - Ivan Marisca; Jacob Bamberger; Cesare Alippi; Michael M. Bronstein (CoRR, 2025)

Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights. - Zhiyuan Liang; Dongwen Tang; Yuhao Zhou; Xuanlei Zhao; Mingjia Shi; Wangbo Zhao; Zekai Li; Peihao Wang; Konstantin Schürholt; Damian Borth; Michael M. Bronstein; Yang You 001; et al. (CoRR, 2025)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jungyoon Lee; Avishek Joey Bose; Valentin De Bortoli; Arnaud Doucet; Michael M. Bronstein; Dominique Beaini; Siamak Ravanbakhsh; Kirill Neklyudov; Alexander Tong 001 (CoRR, 2025)

GradMetaNet: An Equivariant Architecture for Learning on Gradients. - Yoav Gelberg; Yam Eitan; Aviv Navon; Aviv Shamsian; Theo Putterman; Michael M. Bronstein; Haggai Maron (CoRR, 2025)

Mathematical Foundations of Geometric Deep Learning. - Haitz Sáez de Ocáriz Borde; Michael M. Bronstein (CoRR, 2025)