Research map / Architectures and efficiency / RNNs and normalization
Towards Theoretically Inspired Neural Initialization Optimization
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
- From Activation to Initialization: Scaling Insights for Optimizing Neural Fields (CVPR 2024 · similarity 0.89)
- Sinusoidal Initialization, Time for a New Start (NeurIPS 2025 · similarity 0.85)
- Principled Architecture-aware Scaling of Hyperparameters (ICLR 2024 · similarity 0.85)
- GradMetaNet: An Equivariant Architecture for Learning on Gradients (NeurIPS 2025 · similarity 0.84)
- IDInit: A Universal and Stable Initialization Method for Neural Network Training (ICLR 2025 · similarity 0.84)
- Structured Initialization for Vision Transformers (NeurIPS 2025 · similarity 0.84)
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.