Research map / Probabilistic and scientific modeling / Molecules and proteins
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the semi-supervised setting with 50% labeled data instead of the full-label setting | Ours on Colored-MNIST · Colored-MNIST test env. | ACC: 62.1 ± 6.1 -> 58.7 ± 7.2 | Mixed |
Closest papers by meaning
- Learning Discrete Concepts in Latent Hierarchical Models (NeurIPS 2024 · similarity 0.86)
- Linear combinations of latents in generative models: subspaces and beyond (ICLR 2025 · similarity 0.86)
- Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations (ICML 2025 · similarity 0.86)
- Learning Discrete Latent Variable Structures with Tensor Rank Conditions (NeurIPS 2024 · similarity 0.86)
- GLAD: Improving Latent Graph Generative Modeling with Simple Quantization (AAAI 2025 · similarity 0.85)
- Probing the Latent Hierarchical Structure of Data via Diffusion Models (ICLR 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.