{"total":32,"rows":[{"id":"2210.16658","title":"Perturbation Analysis of Neural Collapse","created":"2022-10-29","cited_by_count":35,"area":null},{"id":"2210.17216","title":"Symmetries, flat minima, and the conserved quantities of gradient flow","created":"2022-10-31","cited_by_count":35,"area":null},{"id":"2201.11729","title":"Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks","created":"2022-01-27","cited_by_count":27,"area":null},{"id":"2310.20703","title":"Vanishing Gradients in Reinforcement Finetuning of Language Models","created":"2023-10-31","cited_by_count":27,"area":null},{"id":"2108.02040","title":"Convergence of gradient descent for learning linear neural networks","created":"2021-08-04","cited_by_count":22,"area":null},{"id":"2503.12811","title":"A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules","created":"2025-03-17","cited_by_count":22,"area":null},{"id":"2205.09072","title":"On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias","created":"2022-05-18","cited_by_count":21,"area":null},{"id":"2305.13064","title":"Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond","created":"2023-05-22","cited_by_count":21,"area":null},{"id":"2305.16427","title":"Neural (Tangent Kernel) Collapse","created":"2023-05-25","cited_by_count":18,"area":null},{"id":"2210.15898","title":"Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis","created":"2022-10-28","cited_by_count":13,"area":null},{"id":"2302.01952","title":"On a continuous time model of gradient descent dynamics and instability in deep learning","created":"2023-02-03","cited_by_count":11,"area":null},{"id":"2205.15142","title":"Special Properties of Gradient Descent with Large Learning Rates","created":"2022-05-30","cited_by_count":10,"area":null},{"id":"2207.10283","title":"One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training","created":"2022-07-21","cited_by_count":7,"area":null},{"id":"2210.14064","title":"Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets","created":"2022-10-25","cited_by_count":7,"area":null},{"id":"2402.17759","title":"Towards Optimal Learning of Language Models","created":"2024-02-27","cited_by_count":7,"area":null},{"id":"2210.06705","title":"From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent","created":"2022-10-13","cited_by_count":5,"area":null},{"id":"2310.19919","title":"Meta-Learning Strategies through Value Maximization in Neural Networks","created":"2023-10-30","cited_by_count":4,"area":null},{"id":"2411.05746","title":"Continuous-Time Analysis of Adaptive Optimization and Normalization","created":"2024-11-08","cited_by_count":4,"area":null},{"id":"2501.18582","title":"A Control Perspective on Training PINNs","created":"2025-01-30","cited_by_count":3,"area":null},{"id":"2510.21292","title":"Additive Models Explained: A Computational Complexity Approach","created":"2025-10-24","cited_by_count":3,"area":null},{"id":"2308.01621","title":"A Novel Convolutional Neural Network Architecture with a Continuous Symmetry","created":"2023-08-03","cited_by_count":2,"area":null},{"id":"2503.22478","title":"Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent","created":"2025-03-28","cited_by_count":2,"area":null},{"id":"2504.15323","title":"HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2201.12052","title":"Improved Overparametrization Bounds for Global Convergence of Stochastic Gradient Descent for Shallow Neural Networks","created":"2022-01-28","cited_by_count":1,"area":null},{"id":"2402.13810","title":"The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank","created":"2024-02-21","cited_by_count":1,"area":null},{"id":"2408.13767","title":"Lecture Notes on Linear Neural Networks: A Tale of Optimization and Generalization in Deep Learning","created":"2024-08-25","cited_by_count":1,"area":null},{"id":"2501.17284","title":"Nonlinear dynamics of localization in neural receptive fields","created":"2025-01-28","cited_by_count":1,"area":null},{"id":"2602.13910","title":"Sufficient Conditions for Stability of Minimum-Norm Interpolating Deep ReLU Networks","created":"2026-02-14","cited_by_count":1,"area":null},{"id":"2402.08818","title":"Corridor Geometry in Gradient-Based Optimization","created":"2024-02-13","cited_by_count":0,"area":null},{"id":"2406.11872","title":"The EarlyBird Gets the WORM: Heuristically Accelerating EarlyBird Convergence","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2509.23887","title":"Gradient Flow Convergence Guarantee for General Neural Network Architectures","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2603.02095","title":"On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective","created":"2026-03-02","cited_by_count":0,"area":null}],"next_cursor":null}