gradient flow

Improved memory in recurrent neural networks with sequential non-normal dynamics ICLR 2020 MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks CVPR 2020 Ode to an ODE NeurIPS 2020 On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels COLT 2020 On the training dynamics of deep networks with L_2 regularization NeurIPS 2020 Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets NeurIPS 2020 Picking Winning Tickets Before Training by Preserving Gradient Flow ICLR 2020 Revisiting the Landscape of Matrix Factorization AISTATS 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020 Training Linear Neural Networks: Non-Local Convergence and Complexity Results ICML 2020 Why and when should you pool? Analyzing Pooling in Recurrent Architectures EMNLP 2020 Accelerated Flow for Probability Distributions ICML 2019 A Continuous-Time View of Early Stopping for Least Squares Regression AISTATS 2019 Deep Generative Learning via Variational Gradient Flow ICML 2019 Gradient Dynamics of Shallow Univariate ReLU Networks NeurIPS 2019 Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit COLT 2019 Self-Teaching Networks Interspeech 2019 Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions ICML 2019 Sobolev Descent AISTATS 2019 Understanding MCMC Dynamics as Flows on the Wasserstein Space ICML 2019 Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models NeurIPS 2019 Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced NeurIPS 2018 Convergence of Langevin MCMC in KL-divergence ALT 2018 Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem COLT 2018 SobolevFusion: 3D Reconstruction of Scenes Undergoing Free Non-Rigid Motion CVPR 2018