Research map / Optimization / Implicit bias and generalization
Gradient Descent with Large Step Size Restores Symmetry in Deep Linear Networks with Multi-Pathway
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Flatland: The Adventures of Gradient Descent with Large Step Sizes (ICML 2026 · similarity 0.89)
- Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow (ICLR 2023 · similarity 0.88)
- Step Size Matters in Deep Learning (NeurIPS 2018 · similarity 0.87)
- Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization (NeurIPS 2024 · similarity 0.86)
- Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer (ICML 2025 · similarity 0.86)
- Generalized Linear Mode Connectivity for Transformers (NeurIPS 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
