{"total":30,"rows":[{"id":"2409.14623","title":"From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks","created":"2024-09-22","cited_by_count":49,"area":null},{"id":"2406.06158","title":"Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning","created":"2024-06-10","cited_by_count":42,"area":null},{"id":"2405.16002","title":"Does SGD really happen in tiny subspaces?","created":"2024-05-25","cited_by_count":36,"area":null},{"id":"2407.20199","title":"Emergence in non-neural models: grokking modular arithmetic via average gradient outer product","created":"2024-07-29","cited_by_count":28,"area":null},{"id":"2311.02076","title":"Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos","created":"2023-11-03","cited_by_count":22,"area":null},{"id":"2402.13728","title":"Average gradient outer product as a mechanism for deep neural collapse","created":"2024-02-21","cited_by_count":22,"area":null},{"id":"2310.01687","title":"From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression","created":"2023-10-02","cited_by_count":17,"area":null},{"id":"2406.05033","title":"Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes","created":"2024-06-07","cited_by_count":14,"area":null},{"id":"2506.06489","title":"Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks","created":"2025-06-06","cited_by_count":14,"area":null},{"id":"2402.05271","title":"Feature learning as alignment: a structural property of gradient descent in non-linear neural networks","created":"2024-02-07","cited_by_count":10,"area":null},{"id":"2603.05002","title":"Non-Euclidean Gradient Descent Operates at the Edge of Stability","created":"2026-03-05","cited_by_count":9,"area":null},{"id":"2310.17074","title":"Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates","created":"2023-10-26","cited_by_count":8,"area":null},{"id":"2509.25351","title":"Gradient Descent with Large Step Sizes: Chaos and Fractal Convergence Region","created":"2025-09-29","cited_by_count":7,"area":null},{"id":"2507.12837","title":"Understanding the Evolution of the Neural Tangent Kernel at the Edge of Stability","created":"2025-07-17","cited_by_count":6,"area":null},{"id":"2506.06940","title":"Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2503.23642","title":"Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent","created":"2025-03-31","cited_by_count":4,"area":null},{"id":"2311.15051","title":"Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults","created":"2023-11-25","cited_by_count":3,"area":null},{"id":"2510.25244","title":"BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training","created":"2025-10-29","cited_by_count":3,"area":null},{"id":"2507.05644","title":"The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations","created":"2025-07-08","cited_by_count":2,"area":null},{"id":"2507.17748","title":"Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility","created":"2025-07-23","cited_by_count":2,"area":null},{"id":"2510.15563","title":"On the Neural Feature Ansatz for Deep Neural Networks","created":"2025-10-17","cited_by_count":2,"area":null},{"id":"2507.06464","title":"SoftSignSGD(S3): An Enhanced Optimizer for Practical DNN Training and Loss Spikes Minimization Beyond Adam","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2603.10079","title":"Large Spikes in Stochastic Gradient Descent: A Large-Deviations View","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2604.00316","title":"Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2608.18415","title":"The Road Taken: The Role of Optimizers at the Edge of Stability","created":"2026-08-19","cited_by_count":1,"area":null},{"id":"2608.19762","title":"Finite-Horizon Input-Output Dynamics of Minibatch Perturbations in AdamW","created":"2026-08-20","cited_by_count":1,"area":null},{"id":"2502.13110","title":"Feature Learning Beyond the Edge of Stability","created":"2025-02-18","cited_by_count":0,"area":null},{"id":"2605.29983","title":"Improving Adversarial Robustness of Attribution via Implicit Regularization","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.05219","title":"Gradient Descent with Large Step Size Restores Symmetry in Deep Linear Networks with Multi-Pathway","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2610.13524","title":"Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}