Analysis of a Two-Layer Neural Network via Displacement Convexity

5 January 2019

Papers citing "Analysis of a Two-Layer Neural Network via Displacement Convexity"

47 / 47 papers shown

Title
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime Francesco Camilli D. Tieplova Eleonora Bergamin Jean Barbier 97 0 0 06 May 2025
Kernel Approximation of Fisher-Rao Gradient Flows Jia Jie Zhu Alexander Mielke 44 5 0 27 Oct 2024
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior Anming Gu Edward Chien Kristjan Greenewald 44 4 0 11 Jun 2024
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport Raphael Barboni Gabriel Peyré Franccois-Xavier Vialard 32 3 0 19 Mar 2024
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features Simone Bombari Marco Mondelli 31 3 0 05 Feb 2024
Fundamental limits of overparametrized shallow neural networks for supervised learning Francesco Camilli D. Tieplova Jean Barbier 22 9 0 11 Jul 2023
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction Taiji Suzuki Denny Wu Atsushi Nitanda 27 16 0 12 Jun 2023
Doubly Regularized Entropic Wasserstein Barycenters Lénaïc Chizat 13 11 0 21 Mar 2023
Generalization and Stability of Interpolating Neural Networks with Minimal Width Hossein Taheri Christos Thrampoulidis 24 15 0 18 Feb 2023
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent Benjamin Gess Sebastian Kassing Vitalii Konarovskyi DiffM 24 6 0 14 Feb 2023
Efficient displacement convex optimization with particle gradient descent Hadi Daneshmand J. Lee Chi Jin 21 5 0 09 Feb 2023
Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics Yulong Lu D. Slepčev Lihan Wang 32 22 0 01 Nov 2022
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 10 5 0 28 Oct 2022
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence Diyuan Wu Vyacheslav Kungurtsev Marco Mondelli 15 3 0 13 Oct 2022
Neural Networks can Learn Representations with Gradient Descent Alexandru Damian Jason D. Lee Mahdi Soltanolkotabi SSL MLT 17 112 0 30 Jun 2022
Sharp asymptotics on the compression of two-layer neural networks Mohammad Hossein Amani Simone Bombari Marco Mondelli Rattana Pukdee Stefano Rini MLT 17 0 0 17 May 2022
Trajectory Inference via Mean-field Langevin in Path Space Lénaïc Chizat Stephen X. Zhang Matthieu Heitz Geoffrey Schiebinger 20 20 0 14 May 2022
On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 20 12 0 22 Apr 2022
A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling Katy Craig Karthik Elamvazhuthi M. Haberland O. Turanova 25 15 0 25 Feb 2022
Convex Analysis of the Mean Field Langevin Dynamics Atsushi Nitanda Denny Wu Taiji Suzuki MLT 59 64 0 25 Jan 2022
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic Yufeng Zhang Siyu Chen Zhuoran Yang Michael I. Jordan Zhaoran Wang 58 4 0 27 Dec 2021
Global convergence of ResNets: From finite to infinite width using linear parameterization Raphael Barboni Gabriel Peyré Franccois-Xavier Vialard 11 12 0 10 Dec 2021
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks A. Shevchenko Vyacheslav Kungurtsev Marco Mondelli MLT 30 13 0 03 Nov 2021
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training H. Pham Phan-Minh Nguyen 6 6 0 29 Oct 2021
Convergence rates for shallow neural networks learned by gradient descent Alina Braun Michael Kohler S. Langer Harro Walk 20 10 0 20 Jul 2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction Dominik Stöger Mahdi Soltanolkotabi ODL 31 74 0 28 Jun 2021
Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis Stephan Wojtowytsch 23 33 0 04 Jun 2021
Global Convergence of Three-layer Neural Networks in the Mean Field Regime H. Pham Phan-Minh Nguyen MLT AI4CE 38 19 0 11 May 2021
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis Atsushi Nitanda Denny Wu Taiji Suzuki 6 29 0 31 Dec 2020
Dataset Dynamics via Gradient Flows in Probability Space David Alvarez-Melis Nicolò Fusi 13 18 0 24 Oct 2020
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime Andrea Agazzi Jianfeng Lu 13 15 0 22 Oct 2020
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't E. Weinan Chao Ma Stephan Wojtowytsch Lei Wu AI4CE 6 133 0 22 Sep 2020
Quantitative Propagation of Chaos for SGD in Wide Neural Networks Valentin De Bortoli Alain Durmus Xavier Fontaine Umut Simsekli 16 25 0 13 Jul 2020
On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures M. Samarin Volker Roth David Belius 6 3 0 24 Jun 2020
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions M. B. Khuzani Yinyu Ye S. Napel Lei Xing 11 1 0 23 Jun 2020
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory Yufeng Zhang Qi Cai Zhuoran Yang Yongxin Chen Zhaoran Wang OOD MLT 58 11 0 08 Jun 2020
Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning M. Fornasier Hui Huang L. Pareschi Philippe Sünnen 6 67 0 31 Jan 2020
A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks Phan-Minh Nguyen H. Pham AI4CE 11 81 0 30 Jan 2020
Avoiding Spurious Local Minima in Deep Quadratic Networks A. Kazemipour Brett W. Larsen S. Druckmann ODL 11 6 0 31 Dec 2019
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks A. Shevchenko Marco Mondelli 19 37 0 20 Dec 2019
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models M. B. Khuzani Liyue Shen Shahin Shahrampour Lei Xing 16 1 0 25 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 39 624 0 14 Aug 2019
Sparse Optimization on Measures with Over-parameterized Gradient Descent Lénaïc Chizat 13 92 0 24 Jul 2019
Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks Kaitong Hu Zhenjie Ren David Siska Lukasz Szpruch MLT 17 104 0 19 May 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 25 136 0 10 Apr 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 11 275 0 16 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks Phan-Minh Nguyen AI4CE 14 72 0 07 Feb 2019