Research map / Probabilistic and scientific modeling / Variational and generative sampling
Conditional Generative Moment-Matching Networks
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 a higher-dimensional hidden space than dimension 5 (ablation) | CGMMN MLP on MNIST prediction · MNIST | error rate | No clear effect |
| Combine Gaussian kernels with different bandwidths (ablation) | CGMMN · Experiments across tasks | performance | No clear effect |
| Add an autoencoder code-space representation before CGMMN generation (ablation) | CGMMN on MNIST generation · MNIST | sample clarity | Helped |
| Distill the PBP teacher predictive distribution with CGMMN | PBP on Boston Housing · Boston Housing test | RMSE: 2.574 ± 0.089 -> 2.580 ± 0.093 | No clear effect |
Closest papers by meaning
- Improving Generative Moment Matching Networks with Distribution Partition (AAAI 2021 · similarity 0.91)
- Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method (NeurIPS 2026 · similarity 0.87)
- Generative Marginalization Models (ICML 2024 · similarity 0.84)
- Neural-Kernel Conditional Mean Embeddings (ICML 2024 · similarity 0.84)
- Inductive Moment Matching (ICML 2025 · similarity 0.84)
- A Theory for Conditional Generative Modeling on Multiple Data Sources (ICML 2025 · similarity 0.83)
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.