Research map / Architectures and efficiency / RNNs and normalization
Topology and geometry of the learning space of ReLU networks: connectivity and singularities
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
- Topological obstruction to the training of shallow ReLU neural networks (NeurIPS 2024 · similarity 0.90)
- Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks (NeurIPS 2024 · similarity 0.89)
- The Symmetries of Three-Layer ReLU Networks (NeurIPS 2026 · similarity 0.88)
- Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding (ICLR 2025 · similarity 0.87)
- Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training (ICML 2025 · similarity 0.87)
- Activation-Descent Regularization for Input Optimization of ReLU Networks (ICML 2024 · similarity 0.87)
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.
