Research map / Architectures and efficiency / Pruning and efficient tuning
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
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
- The Generalization-Stability Tradeoff In Neural Network Pruning (NeurIPS 2020 · similarity 0.91)
- Dataset Pruning: Reducing Training Data by Examining Generalization Influence (ICLR 2023 · similarity 0.90)
- Pruning has a disparate impact on model accuracy (NeurIPS 2022 · similarity 0.89)
- The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws (ICLR 2025 · similarity 0.88)
- Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities (ICML 2024 · similarity 0.87)
- Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision (ICML 2025 · 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.
