Research map / Probabilistic and scientific modeling / Variational and generative sampling
Hierarchical Decompositional Mixtures of Variational Autoencoders
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
- Trading Information between Latents in Hierarchical Variational Autoencoders (ICLR 2023 · similarity 0.93)
- Recursive Inference for Variational Autoencoders (NeurIPS 2020 · similarity 0.92)
- Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders (NeurIPS 2021 · similarity 0.90)
- Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders (ICML 2023 · similarity 0.90)
- Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders (ICLR 2024 · similarity 0.90)
- Variational Learning of Fractional Posteriors (ICML 2025 · similarity 0.90)
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.