Research map / Learning theory and algorithms / Clustering
Amortized Mixing Coupling Processes for Clustering
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 |
|---|---|---|---|
| AMCP instead of DAC | amortized clustering · Synthetic 2D GMM Scenario 1 | clustering accuracy: 0.7923 -> 0.8532 | Helped |
| AMCP instead of NCP | amortized clustering · Synthetic 2D GMM Scenario 2 | clustering accuracy: 0.7233 -> 0.7574 | Helped |
| AMCP instead of ST-ACT | amortized clustering · Synthetic 2D GMM Scenario 1 | NMI: 0.9123 -> 0.9334 | Helped |
| AMCP instead of DAC | amortized clustering · Tiny-ImageNet | clustering accuracy: 0.0907 -> 0.1324 | Helped |
| AMCP instead of DAC | amortized clustering · CIFAR-10 | clustering accuracy: 0.8835 -> 0.9024 | Helped |
| AMCP instead of DAC | amortized clustering · MNIST | clustering accuracy: 0.9796 -> 0.9845 | Helped |
| AMCP instead of ST-ACT | amortized clustering · MNIST | running time (s): 22.56 -> 18.22 | Helped |
| AMCP instead of ST-ACT | amortized clustering · CIFAR-10 | running time (s): 68.32 -> 59.33 | Helped |
| amortized AMCP instead of MCMC | probabilistic clustering · Synthetic 2D GMM Scenario 1 | running time (s): 0.0832 -> 0.0113 | Helped |
| amortized AMCP instead of VI | probabilistic clustering · Synthetic 2D GMM Scenario 1 | running time (s): 0.0332 -> 0.0113 | Helped |
| increase number of EM steps T from 1 to 5 (ablation) | AMCP · Scenario 1 and CIFAR-10 | LL and ACC | Helped |
| increase number of EM steps T from 1 to 5 (ablation) | AMCP · Scenario 1 and CIFAR-10 | running time | Hurt |
Closest papers by meaning
- Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering (CVPR 2021 · similarity 0.87)
- A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering (NeurIPS 2017 · similarity 0.85)
- Parameter-Free Clustering via Self-Supervised Consensus Maximization (AAAI 2026 · similarity 0.85)
- Improved Learning-augmented Algorithms for k-means and k-medians Clustering (ICLR 2023 · similarity 0.84)
- Clustering via Self-Supervised Diffusion (ICML 2025 · similarity 0.83)
- Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures (NeurIPS 2024 · 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.
