Geometric models with co-occurrence groups. - Joan Bruna; Stéphane Mallat (ESANN, 2010)

Classification with scattering operators. - Joan Bruna; Stéphane Mallat (CVPR, 2011)

Classification with invariant scattering representations. - Joan Bruna; Stéphane Mallat (IVMSP, 2011)

Audio Texture Synthesis with Scattering Moments. - Joan Bruna; Stéphane Mallat (CoRR, 2013)

Blind Deconvolution with Re-weighted Sparsity Promotion. - Dilip Krishnan; Joan Bruna; Rob Fergus (CoRR, 2013)

Learning Stable Group Invariant Representations with Convolutional Networks - Joan Bruna; Arthur Szlam; Yann LeCun (ICLR (Workshop Poster), 2013)

Invariant Scattering Convolution Networks. - Joan Bruna; Stéphane Mallat (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Scattering Representations for Recognition. (Representations en Scattering pour la Reconaissance). - Joan Bruna (2013)

Signal recovery from Pooling Representations. - Joan Bruna Estrach; Arthur Szlam; Yann LeCun (ICML, 2014)

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. - Emily L. Denton; Wojciech Zaremba; Joan Bruna; Yann LeCun; Rob Fergus (NIPS, 2014)

Spectral Networks and Locally Connected Networks on Graphs. - Joan Bruna; Wojciech Zaremba; Arthur Szlam; Yann LeCun (ICLR, 2014)

Video (language) modeling: a baseline for generative models of natural videos. - Marc'Aurelio Ranzato; Arthur Szlam; Joan Bruna; Michaël Mathieu; Ronan Collobert; Sumit Chopra (CoRR, 2014)

Intriguing properties of neural networks. - Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian J. Goodfellow; Rob Fergus (ICLR (Poster), 2014)

Source separation with scattering Non-Negative Matrix Factorization. - Joan Bruna; Pablo Sprechmann; Yann LeCun (ICASSP, 2015)

Unsupervised Learning of Spatiotemporally Coherent Metrics. - Ross Goroshin; Joan Bruna; Jonathan Tompson; David Eigen; Yann LeCun (ICCV, 2015)

A theoretical argument for complex-valued convolutional networks. - Joan Bruna; Soumith Chintala; Yann LeCun; Serkan Piantino; Arthur Szlam; Mark Tygert (CoRR, 2015)

Unsupervised Feature Learning from Temporal Data. - Ross Goroshin; Joan Bruna; Jonathan Tompson; David Eigen; Yann LeCun (ICLR (Workshop), 2015)

Deep Convolutional Networks on Graph-Structured Data. - Mikael Henaff; Joan Bruna; Yann LeCun (CoRR, 2015)

Audio Source Separation with Discriminative Scattering Networks. - Pablo Sprechmann; Joan Bruna; Yann LeCun (ICLR (Workshop), 2015)

Super-Resolution with Deep Convolutional Sufficient Statistics. - Joan Bruna; Pablo Sprechmann; Yann LeCun (ICLR (Poster), 2016)

Inverse Problems with Invariant Multiscale Statistics. - Ivan Dokmanic; Joan Bruna; Stéphane Mallat; Maarten V. de Hoop (CoRR, 2016)

Voice Conversion using Convolutional Neural Networks. - Shariq Mobin; Joan Bruna (CoRR, 2016)

Divide and Conquer with Neural Networks. - Alex Nowak; Joan Bruna (CoRR, 2016)

A Mathematical Motivation for Complex-Valued Convolutional Networks. - Mark Tygert; Joan Bruna; Soumith Chintala; Yann LeCun; Serkan Piantino; Arthur Szlam (Neural Comput., 2016)

Topology and Geometry of Half-Rectified Network Optimization. - C. Daniel Freeman; Joan Bruna (ICLR (Poster), 2017)

Understanding Trainable Sparse Coding with Matrix Factorization. - Thomas Moreau 001; Joan Bruna (ICLR (Poster), 2017)

Surface Networks. - Ilya Kostrikov; Joan Bruna; Daniele Panozzo; Denis Zorin (CoRR, 2017)

A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. - Alex Nowak; Soledad Villar; Afonso S. Bandeira; Joan Bruna (CoRR, 2017)

Mathematics of Deep Learning. - René Vidal; Joan Bruna; Raja Giryes; Stefano Soatto (CoRR, 2017)

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

Pommerman: A Multi-Agent Playground. - Cinjon Resnick; Wes Eldridge; David Ha; Denny Britz; Jakob N. Foerster; Julian Togelius; Kyunghyun Cho; Joan Bruna (AIIDE Workshops, 2018)

Surface Networks. - Ilya Kostrikov; Zhongshi Jiang; Daniele Panozzo; Denis Zorin; Joan Bruna (CVPR, 2018)

Revised Note on Learning Quadratic Assignment with Graph Neural Networks. - Alex Nowak; Soledad Villar; Afonso S. Bandeira; Joan Bruna (DSW, 2018)

Divide and Conquer Networks. - Alex Nowak; David Folqué; Joan Bruna (ICLR (Poster), 2018)

Few-Shot Learning with Graph Neural Networks. - Victor Garcia Satorras; Joan Bruna Estrach (ICLR (Poster), 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)

Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys. - Luca Venturi; Afonso S. Bandeira; Joan Bruna (CoRR, 2018)

Backplay: "Man muss immer umkehren". - Cinjon Resnick; Roberta Raileanu; Sanyam Kapoor; Alex Peysakhovich; Kyunghyun Cho; Joan Bruna (CoRR, 2018)

Planning with Arithmetic and Geometric Attributes. - David Folqué; Sainbayar Sukhbaatar; Arthur Szlam; Joan Bruna (CoRR, 2018)

Kymatio: Scattering Transforms in Python. - Mathieu Andreux; Tomás Angles; Georgios Exarchakis; Roberto Leonarduzzi; Gaspar Rochette; Louis Thiry; John Zarka; Stéphane Mallat; Joakim Andén; Eugene Belilovsky; Joan Bruna; Vincent Lostanlen; et al. (CoRR, 2018)

Deep Geometric Prior for Surface Reconstruction. - Francis Williams; Teseo Schneider; Cláudio T. Silva; Denis Zorin; Joan Bruna; Daniele Panozzo (CVPR, 2019)

Supervised Community Detection with Line Graph Neural Networks. - Zhengdao Chen; Lisha Li; Joan Bruna (ICLR (Poster), 2019)

Diffusion Scattering Transforms on Graphs. - Fernando Gama; Alejandro Ribeiro; Joan Bruna (ICLR (Poster), 2019)

Approximating Orthogonal Matrices with Effective Givens Factorization. - Thomas Frerix; Joan Bruna (ICML, 2019)

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (ICML, 2019)

On the equivalence between graph isomorphism testing and function approximation with GNNs. - Zhengdao Chen; Soledad Villar; Lei Chen 062; Joan Bruna (NeurIPS, 2019)

Stability of Graph Scattering Transforms. - Fernando Gama; Alejandro Ribeiro; Joan Bruna (NeurIPS, 2019)

On the Expressive Power of Deep Polynomial Neural Networks. - Joe Kileel 001; Matthew Trager; Joan Bruna (NeurIPS, 2019)

Gradient Dynamics of Shallow Univariate ReLU Networks. - Francis Williams; Matthew Trager; Daniele Panozzo; Cláudio T. Silva; Denis Zorin; Joan Bruna (NeurIPS, 2019)

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. - Stéphane d'Ascoli; Levent Sagun; Giulio Biroli; Joan Bruna (NeurIPS, 2019)

Global convergence of neuron birth-death dynamics. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2019)

Advancing GraphSAGE with A Data-Driven Node Sampling. - Jihun Oh; Kyunghyun Cho; Joan Bruna (CoRR, 2019)

On the Expected Dynamics of Nonlinear TD Learning. - David Brandfonbrener; Joan Bruna (CoRR, 2019)

Extra-gradient with player sampling for provable fast convergence in n-player games. - Samy Jelassi; Carles Domingo-Enrich; Damien Scieur; Arthur Mensch; Joan Bruna (CoRR, 2019)

Probing the State of the Art: A Critical Look at Visual Representation Evaluation. - Cinjon Resnick; Zeping Zhan; Joan Bruna (CoRR, 2019)

Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes. - Luca Venturi; Afonso S. Bandeira; Joan Bruna (J. Mach. Learn. Res., 2019)

Stability of Graph Neural Networks to Relative Perturbations. - Fernando Gama; Alejandro Ribeiro; Joan Bruna (ICASSP, 2020)

Geometric Insights into the Convergence of Nonlinear TD Learning. - David Brandfonbrener; Joan Bruna (ICLR, 2020)

Pure and Spurious Critical Points: a Geometric Study of Linear Networks. - Matthew Trager; Kathlén Kohn; Joan Bruna (ICLR, 2020)

Extra-gradient with player sampling for faster convergence in n-player games. - Samy Jelassi; Carles Domingo-Enrich; Damien Scieur; Arthur Mensch; Joan Bruna (ICML, 2020)

A Rate-Distortion Framework for Explaining Black-Box Model Decisions. - Stefan Kolek; Duc Anh Nguyen; Ron Levie; Joan Bruna; Gitta Kutyniok (xxAI@ICML, 2020)

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. - Yossi Arjevani; Joan Bruna; Bugra Can; Mert Gürbüzbalaban; Stefanie Jegelka; Hongzhou Lin (NeurIPS, 2020)

Can Graph Neural Networks Count Substructures? - Zhengdao Chen; Lei Chen 062; Soledad Villar; Joan Bruna (NeurIPS, 2020)

A Dynamical Central Limit Theorem for Shallow Neural Networks. - Zhengdao Chen; Grant M. Rotskoff; Joan Bruna; Eric Vanden-Eijnden (NeurIPS, 2020)

A mean-field analysis of two-player zero-sum games. - Carles Domingo-Enrich; Samy Jelassi; Arthur Mensch; Grant M. Rotskoff; Joan Bruna (NeurIPS, 2020)

Provably Efficient Third-Person Imitation from Offline Observation. - Aaron Zweig; Joan Bruna (UAI, 2020)

On Sparsity in Overparametrised Shallow ReLU Networks. - Jaume de Dios 001; Joan Bruna (CoRR, 2020)

Overfitting and Optimization in Offline Policy Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 2020)

In-Distribution Interpretability for Challenging Modalities. - Cosmas Heiß; Ron Levie; Cinjon Resnick; Gitta Kutyniok; Joan Bruna (CoRR, 2020)

Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems. - Donsub Rim; Luca Venturi; Joan Bruna; Benjamin Peherstorfer (CoRR, 2020)

Learned Equivariant Rendering without Transformation Supervision. - Cinjon Resnick; Or Litany; Hugo Larochelle; Joan Bruna; Kyunghyun Cho (CoRR, 2020)

Continuous LWE. - Joan Bruna; Oded Regev 001; Min Jae Song; Yi Tang (Electron. Colloquium Comput. Complex., 2020)

Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science. - Joan Bruna; Eldad Haber; Gitta Kutyniok; Thomas Pock; René Vidal (J. Math. Imaging Vis., 2020)

Kymatio: Scattering Transforms in Python. - Mathieu Andreux; Tomás Angles; Georgios Exarchakis; Roberto Leonarduzzi; Gaspar Rochette; Louis Thiry; John Zarka; Stéphane Mallat; Joakim Andén; Eugene Belilovsky; Joan Bruna; Vincent Lostanlen; et al. (J. Mach. Learn. Res., 2020)

Stability Properties of Graph Neural Networks. - Fernando Gama; Joan Bruna; Alejandro Ribeiro (IEEE Trans. Signal Process., 2020)

A Permutation-Equivariant Neural Network Architecture For Auction Design. - Jad Rahme; Samy Jelassi; Joan Bruna; S. Matthew Weinberg (AAAI, 2021)

Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks. - Francis Williams; Matthew Trager; Joan Bruna; Denis Zorin (CVPR, 2021)

Learning the Relevant Substructures for Tasks on Graph Data. - Lei Chen 062; Zhengdao Chen; Joan Bruna (ICASSP, 2021)

On Graph Neural Networks versus Graph-Augmented MLPs. - Lei Chen 062; Zhengdao Chen; Joan Bruna (ICLR, 2021)

Offline Contextual Bandits with Overparameterized Models. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (ICML, 2021)

On Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Eric Vanden-Eijnden; Joan Bruna (ICML, 2021)

A Functional Perspective on Learning Symmetric Functions with Neural Networks. - Aaron Zweig; Joan Bruna (ICML, 2021)

Kernel-Based Smoothness Analysis of Residual Networks. - Tom Tirer; Joan Bruna; Raja Giryes (MSML, 2021)

On the Sample Complexity of Learning under Geometric Stability. - Alberto Bietti; Luca Venturi; Joan Bruna (NeurIPS, 2021)

Offline RL Without Off-Policy Evaluation. - David Brandfonbrener; Will Whitney; Rajesh Ranganath; Joan Bruna (NeurIPS, 2021)

An Extensible Benchmark Suite for Learning to Simulate Physical Systems. - Karl Otness; Arvi Gjoka; Joan Bruna; Daniele Panozzo; Benjamin Peherstorfer; Teseo Schneider; Denis Zorin (NeurIPS Datasets and Benchmarks, 2021)

On the Cryptographic Hardness of Learning Single Periodic Neurons. - Min Jae Song; Ilias Zadik; Joan Bruna (NeurIPS, 2021)

Self-Supervised Equivariant Scene Synthesis from Video. - Cinjon Resnick; Or Litany; Cosmas Heiß; Hugo Larochelle; Joan Bruna; Kyunghyun Cho (CoRR, 2021)

Symmetry Breaking in Symmetric Tensor Decomposition. - Yossi Arjevani; Joan Bruna; Michael Field; Joe Kileel 001; Matthew Trager; Francis Williams (CoRR, 2021)

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

On the Sample Complexity of Learning with Geometric Stability. - Alberto Bietti; Luca Venturi; Joan Bruna (CoRR, 2021)

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Marylou Gabrié; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2021)

Multi-fidelity Stability for Graph Representation Learning. - Yihan He; Joan Bruna (CoRR, 2021)

Quantile Filtered Imitation Learning. - David Brandfonbrener; William F. Whitney; Rajesh Ranganath; Joan Bruna (CoRR, 2021)

Lattice-Based Methods Surpass Sum-of-Squares in Clustering. - Ilias Zadik; Min Jae Song; Alexander S. Wein; Joan Bruna (COLT, 2022)

Neural Fields as Learnable Kernels for 3D Reconstruction. - Francis Williams; Zan Gojcic; Sameh Khamis; Denis Zorin; Joan Bruna; Sanja Fidler; Or Litany (CVPR, 2022)

Cartoon Explanations of Image Classifiers. - Stefan Kolek; Duc Anh Nguyen; Ron Levie; Joan Bruna; Gitta Kutyniok (ECCV (12), 2022)

On feature learning in neural networks with global convergence guarantees. - Zhengdao Chen; Eric Vanden-Eijnden; Joan Bruna (ICLR, 2022)

Extended Unconstrained Features Model for Exploring Deep Neural Collapse. - Tom Tirer; Joan Bruna (ICML, 2022)

Learning single-index models with shallow neural networks. - Alberto Bietti; Joan Bruna; Clayton Sanford; Min Jae Song (NeurIPS, 2022)

When does return-conditioned supervised learning work for offline reinforcement learning? - David Brandfonbrener; Alberto Bietti; Jacob Buckman; Romain Laroche; Joan Bruna (NeurIPS, 2022)

On Non-Linear operators for Geometric Deep Learning. - Grégoire Sergeant-Perthuis; Jakob Maier; Joan Bruna; Edouard Oyallon (NeurIPS, 2022)

Exponential Separations in Symmetric Neural Networks. - Aaron Zweig; Joan Bruna (NeurIPS, 2022)

Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. - Joan Bruna; Benjamin Peherstorfer; Eric Vanden-Eijnden (CoRR, 2022)

On Gradient Descent Convergence beyond the Edge of Stability. - Lei Chen 062; Joan Bruna (CoRR, 2022)

Towards Antisymmetric Neural Ansatz Separation. - Aaron Zweig; Joan Bruna (CoRR, 2022)

A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. - Zhengdao Chen; Eric Vanden-Eijnden; Joan Bruna (CoRR, 2022)

Depth separation beyond radial functions. - Luca Venturi; Samy Jelassi; Tristan Ozuch; Joan Bruna (J. Mach. Learn. Res., 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)

Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks. - Pau Batlle; Joan Bruna; Carlos Fernandez-Granda; Victor M. Preciado (SIAM J. Control. Optim., 2022)

Beyond the Edge of Stability via Two-step Gradient Updates. - Lei Chen 062; Joan Bruna (ICML, 2023)

Conditionally Strongly Log-Concave Generative Models. - Florentin Guth; Etienne Lempereur; Joan Bruna; Stéphane Mallat (ICML, 2023)

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. - David Brandfonbrener; Ofir Nachum; Joan Bruna (NeurIPS, 2023)

A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks. - Vignesh Kothapalli; Tom Tirer; Joan Bruna (NeurIPS, 2023)

On Single-Index Models beyond Gaussian Data. - Aaron Zweig; Loucas Pillaud-Vivien; Joan Bruna (NeurIPS, 2023)

Data-driven multiscale modeling of subgrid parameterizations in climate models. - Karl Otness; Laure Zanna; Joan Bruna (CoRR, 2023)

On Learning Gaussian Multi-index Models with Gradient Flow. - Alberto Bietti; Joan Bruna; Loucas Pillaud-Vivien (CoRR, 2023)

Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (COLT, 2024)

Symmetric Single Index Learning. - Aaron Zweig; Joan Bruna (ICLR, 2024)

Provable Posterior Sampling with Denoising Oracles via Tilted Transport. - Joan Bruna; Jiequn Han (NeurIPS, 2024)

Stochastic Optimal Control Matching. - Carles Domingo-Enrich; Jiequn Han; Brandon Amos; Joan Bruna; Ricky T. Q. Chen (NeurIPS, 2024)

Computational-Statistical Gaps in Gaussian Single-Index Models. - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (CoRR, 2024)

How Truncating Weights Improves Reasoning in Language Models. - Lei Chen 062; Joan Bruna; Alberto Bietti (CoRR, 2024)

Posterior Sampling with Denoising Oracles via Tilted Transport. - Joan Bruna; Jiequn Han (CoRR, 2024)

On the Benefits of Rank in Attention Layers. - Noah Amsel; Gilad Yehudai; Joan Bruna (CoRR, 2024)

Neural Galerkin schemes with active learning for high-dimensional evolution equations. - Joan Bruna; Benjamin Peherstorfer; Eric Vanden-Eijnden (J. Comput. Phys., 2024)

Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. - Margalit Glasgow; Denny Wu; Joan Bruna (COLT, 2025)

Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers. - Lei Chen 062; Joan Bruna; Alberto Bietti (ICLR, 2025)

Quality over Quantity in Attention Layers: When Adding More Heads Hurts. - Noah Amsel; Gilad Yehudai; Joan Bruna (ICLR, 2025)

Geometry and Optimization of Shallow Polynomial Networks. - Yossi Arjevani; Joan Bruna; Joe Kileel 001; Elzbieta Polak; Matthew Trager (CoRR, 2025)

Compositional Reasoning with Transformers, RNNs, and Chain of Thought. - Gilad Yehudai; Noah Amsel; Joan Bruna (CoRR, 2025)

Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos. - Chris Pedersen; Laure Zanna; Joan Bruna (CoRR, 2025)

Survey on Algorithms for multi-index models. - Joan Bruna; Daniel Hsu 001 (CoRR, 2025)

Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time. - Margalit Glasgow; Denny Wu; Joan Bruna (CoRR, 2025)

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models. - Alex Damian; Jason D. Lee; Joan Bruna (CoRR, 2025)