Research map / Probabilistic and scientific modeling / Variational and generative sampling
Canonical normalizing flows for manifold learning
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
- Learning Distributions on Manifolds with Free-Form Flows (NeurIPS 2024 · similarity 0.90)
- Lifting Architectural Constraints of Injective Flows (ICLR 2024 · similarity 0.88)
- Injective flows for star-like manifolds (ICLR 2025 · similarity 0.88)
- Categorical Flow Matching on Statistical Manifolds (NeurIPS 2024 · similarity 0.88)
- Rectifying Categorical Flows on Statistical Manifolds for One-Step Generation (NeurIPS 2026 · similarity 0.88)
- What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion (NeurIPS 2026 · 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.