Research map / Optimization / Optimizers
Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training
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
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima (ICLR 2017 · similarity 0.88)
- An Improved Empirical Fisher Approximation for Natural Gradient Descent (NeurIPS 2024 · similarity 0.88)
- Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator (ICML 2025 · similarity 0.87)
- Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation (ICML 2024 · similarity 0.86)
- Fast Training of Large Kernel Models with Delayed Projections (NeurIPS 2025 · similarity 0.86)
- A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks (NeurIPS 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.