Research map / Optimization / Implicit bias and generalization
Implicit Regularization and Convergence for Weight Normalization
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
- Understanding the Disharmony between Weight Normalization Family and Weight Decay (AAAI 2020 · similarity 0.89)
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (NeurIPS 2016 · similarity 0.89)
- A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent (ICML 2019 · similarity 0.87)
- Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization (ICLR 2022 · similarity 0.87)
- On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective (NeurIPS 2023 · similarity 0.87)
- Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries (ICLR 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.
