gradient flow

Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics NeurIPS 2023 Feature selection and low test error in shallow low-rotation ReLU networks ICLR 2023 Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs NeurIPS 2023 From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks COLT 2023 GANs as Gradient Flows that Converge JMLR 2023 Generalizing Backpropagation for Gradient-Based Interpretability ACL 2023 Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond ICML 2023 How much does Initialization Affect Generalization? ICML 2023 Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data ICLR 2023 Implicit Regularization Towards Rank Minimization in ReLU Networks ALT 2023 Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks AAAI 2023 Incremental Learning in Diagonal Linear Networks JMLR 2023 Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs NeurIPS 2023 Leveraging the two-timescale regime to demonstrate convergence of neural networks NeurIPS 2023 Markovian Sliced Wasserstein Distances: Beyond Independent Projections NeurIPS 2023 Meta-Learning with Adjoint Methods AISTATS 2023 Module-wise Training of Neural Networks via the Minimizing Movement Scheme NeurIPS 2023 Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels ICML 2023 On the Convergence of Gradient Flow on Multi-layer Linear Models ICML 2023 On the geometry of Stein variational gradient descent JMLR 2023 On the spectral bias of two-layer linear networks NeurIPS 2023 Particle algorithms for maximum likelihood training of latent variable models AISTATS 2023 Perturbation Analysis of Neural Collapse ICML 2023 Saddle-to-Saddle Dynamics in Diagonal Linear Networks NeurIPS 2023 Sampling with Mollified Interaction Energy Descent ICLR 2023