Research map / Probabilistic and scientific modeling / Differential equations and PDEs
Sparse Flows: Pruning Continuous-depth Models
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
- Neural Ordinary Differential Equations (NeurIPS 2018 · similarity 0.92)
- Hypersolvers: Toward Fast Continuous-Depth Models (NeurIPS 2020 · similarity 0.90)
- Implicit regularization of deep residual networks towards neural ODEs (ICLR 2024 · similarity 0.87)
- Simulation-Free Training of Neural ODEs on Paired Data (NeurIPS 2024 · similarity 0.86)
- Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities (ICML 2024 · similarity 0.86)
- Layer Compression of Deep Networks with Straight Flows (AAAI 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.
