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Joan Bruna

  1. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
    2021/04/27 by Michael M. Bronstein, Joan Bruna, Bronstein, Michael M. +5 · 7 voices · 125 citations
    Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Computational Geometry (cs.CG) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.CG #cs.CV #cs.LG #stat.ML
  2. Intriguing properties of neural networks
    2013/12/21 by Christian Szegedy, Szegedy, Christian, Wojciech Zaremba +11 · 3 voices · 355 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Neural Networks and Applications #cs.CV #cs.LG #cs.NE
  3. Geometric Deep Learning: Going beyond Euclidean data
    2017/07/01 by Michael M. Bronstein, Joan Bruna, Yann LeCun +2 · 165 citations
    Engineering · Computer Science · #3D Shape Modeling and Analysis #Graph Theory and Algorithms #Computational Geometry and Mesh Generation
  4. Deep Convolutional Networks on Graph-Structured Data
    2015/06/16 by Mikael Henaff, Joan Bruna, Henaff, Mikael +3 · 70 citations
    Computer Science · #Advanced Graph Neural Networks #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE)
  5. Few-Shot Learning with Graph Neural Networks
    2017/11/10 by Víctor García, Joan Bruna, Garcia, Victor +1 · 43 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Machine Learning and Algorithms #Advanced Graph Neural Networks
  6. Mathematics of Deep Learning
    2017/12/13 by Rene Vidal, René Vidal, Joan Bruna +6 · 1 voice · 3 citations
    Computer Science · Engineering · #Neural Networks and Applications #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #cs.CV #cs.LG
  7. Kymatio: Scattering Transforms in Python
    2018/12/28 by Mathieu Andreux, Andreux, Mathieu, Tomás Angles +33 · 1 voice · 3 citations
    #cs.LG #cs.CV #cs.SD #eess.AS #stat.ML
  8. Extended Unconstrained Features Model for Exploring Deep Neural Collapse
    2022/02/16 by Tom Tirer, Joan Bruna, Tirer, Tom +1 · 8 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Machine Learning and Data Classification #Neural Networks and Applications
  9. When does return-conditioned supervised learning work for offline reinforcement learning?
    2022/06/02 by David Brandfonbrener, Brandfonbrener, David, Alberto Bietti +7 · 7 citations
    Computer Science · #Reinforcement Learning in Robotics #Machine Learning and Data Classification #Data Stream Mining Techniques
  10. Stochastic Optimal Control Matching
    2023/12/04 by Carles Domingo-Enrich, Jiequn Han, Domingo-Enrich, Carles +7 · 10 citations
    Computer Science · Mathematics · Physics and Astronomy · #Stochastic Gradient Optimization Techniques #Markov Chains and Monte Carlo Methods #Model Reduction and Neural Networks
  11. Supervised Community Detection with Line Graph Neural Networks
    2017/05/23 by Zhengdao Chen, Xiang Li, Chen, Zhengdao +3 · 3 citations
    Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (stat.ML)
  12. Gradient Dynamics of Shallow Univariate ReLU Networks
    2019/06/18 by Francis Williams, Matthew Trager, Williams, Francis +9 · 4 citations
    Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical methods in engineering #Stochastic Gradient Optimization Techniques
  13. On Learning Gaussian Multi-index Models with Gradient Flow
    2023/10/30 by Alberto Bietti, Bietti, Alberto, Joan Bruna +3 · 5 citations
    Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Statistical Mechanics and Entropy #Statistical Methods and Inference
  14. Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers
    2024/06/05 by Lei Chen, Chen, Lei, Joan Bruna +3 · 6 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Topic Modeling
  15. Pure and Spurious Critical Points: a Geometric Study of Linear Networks
    2019/10/03 by Matthew Trager, Kathlén Kohn, Trager, Matthew +3 · 3 citations
    Mathematics · #Advanced Differential Equations and Dynamical Systems #Algebraic Geometry (math.AG) #FOS: Computer and information sciences #FOS: Mathematics #Graph theory and applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods
  16. IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
    2020/06/11 by Yossi Arjevani, Arjevani, Yossi, Joan Bruna +9 · 3 citations
    Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Distributed Control Multi-Agent Systems
  17. Super-Resolution with Deep Convolutional Sufficient Statistics
    2015/11/18 by Joan Bruna, Pablo Sprechmann, Bruna, Joan +3 · 2 citations
    Computer Science · Engineering · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing Techniques and Applications #Image and Signal Denoising Methods
  18. Depth separation beyond radial functions
    2021/02/02 by Luca Venturi, Venturi, Luca, Samy Jelassi +5 · 3 citations
    Computer Science · #Neural Networks and Applications #Stochastic Gradient Optimization Techniques #Machine Learning and ELM
  19. Geometric Insights into the Convergence of Nonlinear TD Learning
    2019/05/29 by David Brandfonbrener, Brandfonbrener, David, Joan Bruna +1 · 2 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Gene Regulatory Network Analysis #Neural Networks and Applications #Reinforcement Learning in Robotics
  20. On the Cryptographic Hardness of Learning Single Periodic Neurons
    2021/06/20 by Min Jae Song, Song, Min Jae, Ilias Zadik +3 · 2 citations
    Computer Science · #Machine Learning and Algorithms #Adversarial Robustness in Machine Learning #Neural Networks and Applications
  21. Survey on Algorithms for multi-index models
    2025/04/07 by Joan Bruna, Daniel Hsu, Bruna, Joan +1 · 1 voice · 5 citations
    Computer Science · #Neural Networks and Applications #Bayesian Modeling and Causal Inference #Gaussian Processes and Bayesian Inference
  22. Backplay: "Man muss immer umkehren"
    2018/07/18 by Cinjon Resnick, Roberta Răileanu, Resnick, Cinjon +9 · 4 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics
  23. On learning Gaussian multi‐index models with gradient flow part I: General properties and two‐timescale learning
    2025/07/15 by Alberto Bietti, Joan Bruna, Loucas Pillaud‐Vivien · 8 citations
    Computer Science · Mathematics · #Neural Networks and Applications #Gaussian Processes and Bayesian Inference #Statistical Methods and Inference
  24. On Energy-Based Models with Overparametrized Shallow Neural Networks
    2021/04/15 by Carles Domingo-Enrich, Alberto Bietti, Domingo-Enrich, Carles +5 · 1 citation
    Physics and Astronomy · Computer Science · Materials Science · #Model Reduction and Neural Networks #Generative Adversarial Networks and Image Synthesis #Machine Learning in Materials Science
  25. On the Sample Complexity of Learning under Invariance and Geometric\n Stability
    2021/06/13 by Alberto Bietti, Luca Venturi, Bietti, Alberto +3 · 1 citation
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
  26. Voice Conversion using Convolutional Neural Networks
    2016/10/27 by Shariq Mobin, Mobin, Shariq, Joan Bruna +1 · 1 citation
    Computer Science · #Music and Audio Processing #Speech and Audio Processing #Music Technology and Sound Studies
  27. Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
    2019/06/16 by Stéphane d’Ascoli, d'Ascoli, Stéphane, Levent Sagun +5 · 2 citations
    Computer Science · #Advanced Neural Network Applications #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Physical sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Statistical Mechanics (cond-mat.stat-mech) #Stochastic Gradient Optimization Techniques
  28. A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
    2023/07/04 by Vignesh Kothapalli, Kothapalli, Vignesh, Tom Tirer +3 · 1 citation
    Computer Science · Neuroscience · #Advanced Graph Neural Networks #Functional Brain Connectivity Studies #Neural dynamics and brain function
  29. On Single Index Models beyond Gaussian Data
    2023/07/28 by Joan Bruna, Bruna, Joan, Loucas Pillaud‐Vivien +3 · 1 citation
    Computer Science · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
  30. The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models
    2025/06/05 by Alex Damian, Damian, Alex, Jason D. Lee +3 · 2 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Tensor decomposition and applications
  31. On Graph Neural Networks versus Graph-Augmented MLPs
    2020/10/28 by Lei Chen, Zhengdao Chen, Chen, Lei +3 · 1 citation
    Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Machine Learning and Algorithms
  32. Kernel-Based Smoothness Analysis of Residual Networks
    2020/09/21 by Tom Tirer, Joan Bruna, Tirer, Tom +3 · 1 citation
    Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Sparse and Compressive Sensing Techniques