{"total":22,"rows":[{"id":"2406.09405","title":"Why Warmup the Learning Rate? Underlying Mechanisms and Improvements","created":"2024-06-13","cited_by_count":46,"area":null},{"id":"2402.17457","title":"Super Consistency of Neural Network Landscapes and Learning Rate Transfer","created":"2024-02-27","cited_by_count":23,"area":null},{"id":"2607.21716","title":"A Defense of the Quadratic Model","created":"2026-07-23","cited_by_count":14,"area":null},{"id":"2410.04642","title":"The Optimization Landscape of SGD Across the Feature Learning Strength","created":"2024-10-06","cited_by_count":13,"area":null},{"id":"2509.25087","title":"Scaling with Collapse: Efficient and Predictable Training of LLM Families","created":"2025-09-29","cited_by_count":9,"area":null},{"id":"2407.06183","title":"Stepping on the Edge: Curvature Aware Learning Rate Tuners","created":"2024-07-08","cited_by_count":8,"area":null},{"id":"2601.16979","title":"A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs","created":"2026-01-23","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":"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":"2401.07085","title":"Three Mechanisms of Feature Learning in a Linear Network","created":"2024-01-13","cited_by_count":4,"area":null},{"id":"2510.17506","title":"Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2506.12903","title":"Variational Learning Finds Flatter Solutions at the Edge of Stability","created":"2025-06-15","cited_by_count":2,"area":null},{"id":"2509.25380","title":"Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2604.19740","title":"Generalization at the Edge of Stability","created":"2026-04-21","cited_by_count":2,"area":null},{"id":"2605.21486","title":"Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate","created":"2026-05-20","cited_by_count":2,"area":null},{"id":"2604.21016","title":"SGD at the Edge of Stability: The Stochastic Sharpness Gap","created":"2026-04-22","cited_by_count":1,"area":null},{"id":"2607.08380","title":"Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima","created":"2026-07-09","cited_by_count":1,"area":null},{"id":"2608.20638","title":"Adam at the Edge of Stability: Adaptive Feedback, Provable Oscillation, and Gradient Reversal","created":"2026-08-21","cited_by_count":1,"area":null},{"id":"2604.18202","title":"Centre manifold theorem for maps along manifolds of fixed points","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2607.13631","title":"How the Hessian-Spectrum of Neural Networks Depends on Data","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2609.01034","title":"The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.32290","title":"A Journey to the Edge of Stability","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}