Abide by the Law and Follow the Flow: Conservation Laws for Gradient Flows
Fuente:
arXiv
Saved in:
| Main Authors: | Marcotte, Sibylle, Gribonval, Rémi, Peyré, Gabriel |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
by: Marcotte, Sibylle, et al.
Published: (2024)
by: Marcotte, Sibylle, et al.
Published: (2024)
Transformative or Conservative? Conservation laws for ResNets and Transformers
by: Marcotte, Sibylle, et al.
Published: (2025)
by: Marcotte, Sibylle, et al.
Published: (2025)
Intrinsic training dynamics of deep neural networks
by: Marcotte, Sibylle, et al.
Published: (2025)
by: Marcotte, Sibylle, et al.
Published: (2025)
Muon Dynamics as a Spectral Wasserstein Flow
by: Peyré, Gabriel
Published: (2026)
by: Peyré, Gabriel
Published: (2026)
Path-conditioned training: a principled way to rescale ReLU neural networks
by: Lebeurrier, Arthur, et al.
Published: (2026)
by: Lebeurrier, Arthur, et al.
Published: (2026)
Robust Sublinear Convergence Rates for Iterative Bregman Projections
by: Peyré, Gabriel
Published: (2026)
by: Peyré, Gabriel
Published: (2026)
Optimal and Diffusion Transports in Machine Learning
by: Peyré, Gabriel
Published: (2025)
by: Peyré, Gabriel
Published: (2025)
Optimal Transport for Machine Learners
by: Peyré, Gabriel
Published: (2025)
by: Peyré, Gabriel
Published: (2025)
Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
by: Kunstner, Frederik, et al.
Published: (2025)
by: Kunstner, Frederik, et al.
Published: (2025)
On the global convergence of gradient descent for wide shallow models with bounded nonlinearities
by: Petit, Romain, et al.
Published: (2026)
by: Petit, Romain, et al.
Published: (2026)
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)
by: Bonet, Clément, et al.
Published: (2025)
GANs as Gradient Flows that Converge
by: Huang, Yu-Jui, et al.
Published: (2022)
by: Huang, Yu-Jui, et al.
Published: (2022)
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport
by: Barboni, Raphaël, et al.
Published: (2024)
by: Barboni, Raphaël, et al.
Published: (2024)
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
by: Barboni, Raphaël, et al.
Published: (2025)
by: Barboni, Raphaël, et al.
Published: (2025)
Safe Gradient Flow for Bilevel Optimization
by: Sharifi, Sina, et al.
Published: (2025)
by: Sharifi, Sina, et al.
Published: (2025)
Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks
by: Beneventano, Pierfrancesco, et al.
Published: (2025)
by: Beneventano, Pierfrancesco, et al.
Published: (2025)
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
by: Ren, Yinuo, et al.
Published: (2023)
by: Ren, Yinuo, et al.
Published: (2023)
Gradient Flow Polarizes Softmax Outputs towards Low-Entropy Solutions
by: Varre, Aditya, et al.
Published: (2026)
by: Varre, Aditya, et al.
Published: (2026)
Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
by: Yamamoto, Naoya, et al.
Published: (2025)
by: Yamamoto, Naoya, et al.
Published: (2025)
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates
by: Baranwal, Mayank, et al.
Published: (2024)
by: Baranwal, Mayank, et al.
Published: (2024)
Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin
by: Kumar, Akshay, et al.
Published: (2025)
by: Kumar, Akshay, et al.
Published: (2025)
Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective
by: Zhu, Jia-Jie
Published: (2024)
by: Zhu, Jia-Jie
Published: (2024)
Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data
by: Min, Hancheng, et al.
Published: (2025)
by: Min, Hancheng, et al.
Published: (2025)
Optimization Hyper-parameter Laws for Large Language Models
by: Xie, Xingyu, et al.
Published: (2024)
by: Xie, Xingyu, et al.
Published: (2024)
Online Markov Decision Processes with Terminal Law Constraints
by: Moreno, Bianca Marin, et al.
Published: (2026)
by: Moreno, Bianca Marin, et al.
Published: (2026)
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
by: Hagemann, Paul, et al.
Published: (2023)
by: Hagemann, Paul, et al.
Published: (2023)
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
by: Altekrüger, Fabian, et al.
Published: (2023)
by: Altekrüger, Fabian, et al.
Published: (2023)
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
by: Kassing, Sebastian, et al.
Published: (2025)
by: Kassing, Sebastian, et al.
Published: (2025)
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)
by: Zhang, Zhengxin, et al.
Published: (2024)
Gradient Flow Sampler-based Distributionally Robust Optimization
by: Xu, Zusen, et al.
Published: (2025)
by: Xu, Zusen, et al.
Published: (2025)
Muon in Associative Memory Learning: Training Dynamics and Scaling Laws
by: Li, Binghui, et al.
Published: (2026)
by: Li, Binghui, et al.
Published: (2026)
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
by: Hurault, Samuel, et al.
Published: (2025)
by: Hurault, Samuel, et al.
Published: (2025)
Implicit Regularization of Gradient Flow on One-Layer Softmax Attention
by: Sheen, Heejune, et al.
Published: (2024)
by: Sheen, Heejune, et al.
Published: (2024)
Training Infinitely Deep and Wide Transformers
by: Barboni, Raphaël, et al.
Published: (2026)
by: Barboni, Raphaël, et al.
Published: (2026)
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
by: Lei, Yuan-Zheng, et al.
Published: (2025)
by: Lei, Yuan-Zheng, et al.
Published: (2025)
Rod Flow: A Continuous-Time Model for Gradient Descent at the Edge of Stability
by: Regis, Eric, et al.
Published: (2026)
by: Regis, Eric, et al.
Published: (2026)
Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
by: Boustany, Samer El, et al.
Published: (2026)
by: Boustany, Samer El, et al.
Published: (2026)
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
by: Ozaslan, Ibrahim K., et al.
Published: (2024)
by: Ozaslan, Ibrahim K., et al.
Published: (2024)
Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws
by: Wang, Jinbo, et al.
Published: (2026)
by: Wang, Jinbo, et al.
Published: (2026)
Provable Mixed-Noise Learning with Flow-Matching
by: Hagemann, Paul, et al.
Published: (2025)
by: Hagemann, Paul, et al.
Published: (2025)
Similar Items
-
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
by: Marcotte, Sibylle, et al.
Published: (2024) -
Transformative or Conservative? Conservation laws for ResNets and Transformers
by: Marcotte, Sibylle, et al.
Published: (2025) -
Intrinsic training dynamics of deep neural networks
by: Marcotte, Sibylle, et al.
Published: (2025) -
Muon Dynamics as a Spectral Wasserstein Flow
by: Peyré, Gabriel
Published: (2026) -
Path-conditioned training: a principled way to rescale ReLU neural networks
by: Lebeurrier, Arthur, et al.
Published: (2026)