Research map / Probabilistic and scientific modeling / Variational and generative sampling
Structured Flow Autoencoders: Learning Structured Probabilistic Representations with Flow Matching
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
- Structured Coupling for Flow Matching (NeurIPS 2026 · similarity 0.91)
- Automatic variational inference with cascading flows (ICML 2021 · similarity 0.88)
- MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series (NeurIPS 2026 · similarity 0.86)
- SDFlow: Similarity-Driven Flow Matching for Time Series Generation (NeurIPS 2026 · similarity 0.86)
- Discrete Flow Matching (NeurIPS 2024 · similarity 0.85)
- Normalizing Flows are Capable Generative Models (ICML 2025 · similarity 0.85)
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.
