Research map / Learning theory and algorithms / Sample complexity
Hardness of Learning Neural Networks with Natural Weights
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
- Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy (NeurIPS 2023 · similarity 0.91)
- On the Hardness of Training Deep Neural Networks Discretely (AAAI 2025 · similarity 0.87)
- Neural Networks with Small Weights and Depth-Separation Barriers (NeurIPS 2020 · similarity 0.85)
- Hardness of Learning Neural Networks under the Manifold Hypothesis (NeurIPS 2024 · similarity 0.84)
- How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers (ICML 2024 · similarity 0.83)
- Weak-to-Strong Generalization Even in Random Feature Networks, Provably (ICML 2025 · similarity 0.83)
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.