Mean-Field Neural ODEs via Relaxed Optimal Control

11 December 2019

Papers citing "Mean-Field Neural ODEs via Relaxed Optimal Control"

37 / 37 papers shown

Title
Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble Atsushi Nitanda Anzelle Lee Damian Tan Xing Kai Mizuki Sakaguchi Taiji Suzuki AI4CE 89 1 0 09 Feb 2025
Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample size Huafu Liao Alpár R. Mészáros Chenchen Mou Chao Zhou 46 2 0 08 Apr 2024
Large-time asymptotics in deep learning Carlos Esteve Borjan Geshkovski Dario Pighin Enrique Zuazua 77 34 0 06 Aug 2020
When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari 76 188 0 24 Jun 2020
Gradient Flows for Regularized Stochastic Control Problems David Siska Lukasz Szpruch 48 21 0 10 Jun 2020
Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov dynamics Belinda Tzen Maxim Raginsky 28 17 0 05 Feb 2020
Machine Learning from a Continuous Viewpoint E. Weinan Chao Ma Lei Wu 60 104 0 30 Dec 2019
Mean-field Langevin System, Optimal Control and Deep Neural Networks Kaitong Hu A. Kazeykina Zhenjie Ren 33 15 0 16 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 73 631 0 14 Aug 2019
Stochastic Gradient and Langevin Processes Xiang Cheng Dong Yin Peter L. Bartlett Michael I. Jordan 24 5 0 07 Jul 2019
The Barron Space and the Flow-induced Function Spaces for Neural Network Models E. Weinan Chao Ma Lei Wu 56 110 0 18 Jun 2019
Statistical Inference for Generative Models with Maximum Mean Discrepancy François‐Xavier Briol Alessandro Barp Andrew B. Duncan Mark Girolami 44 72 0 13 Jun 2019
Maximum Mean Discrepancy Gradient Flow Michael Arbel Anna Korba Adil Salim Arthur Gretton 83 163 0 11 Jun 2019
Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks Kaitong Hu Zhenjie Ren David Siska Lukasz Szpruch MLT 41 105 0 19 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 39 243 0 27 Apr 2019
Augmented Neural ODEs Emilien Dupont Arnaud Doucet Yee Whye Teh BDL 85 622 0 02 Apr 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 64 277 0 16 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 137 966 0 24 Jan 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 174 1,628 0 28 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 87 823 0 19 Dec 2018
Non-asymptotic bounds for sampling algorithms without log-concavity Mateusz B. Majka Aleksandar Mijatović Lukasz Szpruch 33 73 0 21 Aug 2018
A Mean-Field Optimal Control Formulation of Deep Learning Weinan E Jiequn Han Qianxiao Li OOD 48 183 0 03 Jul 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 232 5,024 0 19 Jun 2018
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport Lénaïc Chizat Francis R. Bach OT 157 731 0 24 May 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 76 855 0 18 Apr 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors Gintare Karolina Dziugaite Daniel M. Roy MLT 50 145 0 26 Dec 2017
Geometrical Insights for Implicit Generative Modeling Léon Bottou Martín Arjovsky David Lopez-Paz Maxime Oquab 43 49 0 21 Dec 2017
Maximum Principle Based Algorithms for Deep Learning Qianxiao Li Long Chen Cheng Tai E. Weinan 47 222 0 26 Oct 2017
Geometry of Optimization and Implicit Regularization in Deep Learning Behnam Neyshabur Ryota Tomioka Ruslan Salakhutdinov Nathan Srebro AI4CE 47 132 0 08 May 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 269 4,620 0 10 Nov 2016
Understanding Deep Convolutional Networks S. Mallat FAtt AI4CE 99 639 0 19 Jan 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 56 410 0 17 Jul 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 249 747 0 06 Jun 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 132 2,775 0 20 Feb 2015
On the rate of convergence in Wasserstein distance of the empirical measure N. Fournier Arnaud Guillin 117 1,141 0 07 Dec 2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 135 4,210 0 04 Jun 2013