Research map / Optimization / Implicit bias and generalization
Continuous vs. Discrete Optimization of Deep Neural Networks
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 Optimization in Deep Learning with Central Flows (ICLR 2025 · similarity 0.90)
- Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis (NeurIPS 2022 · similarity 0.89)
- Exact natural gradient in deep linear networks and its application to the nonlinear case (NeurIPS 2018 · similarity 0.89)
- Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization (ICLR 2020 · similarity 0.89)
- No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths (ICML 2024 · similarity 0.87)
- Deconstructing the Goldilocks Zone of Neural Network Initialization (ICML 2024 · 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.