Research map / Probabilistic and scientific modeling / Molecules and proteins
Scalable inference of topic evolution via models for latent geometric structures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model temporal topic evolution with SDM rather than CoSAC | EJC · EJC | Perplexity: 1191 -> 1179 | Helped |
| Model temporal topic evolution with SDDM rather than DM | Wikipedia · Wikipedia | Perplexity: 1260 -> 1201 | Helped |
| Use SDDM instead of SDM, adding group-based processing | EJC · EJC | Perplexity: 1179 -> 1241 | Hurt |
| Use SDDM instead of SDM, adding parallel group processing | Wikipedia · Wikipedia | Time: ~6X faster | Helped |
| Use DM instead of CoSAC, processing groups in parallel and matching outputs | Wikipedia · Wikipedia | Time: ~25X faster | Helped |
| Decrease tau0 to allow faster-changing global topics (ablation) | SDM · EJC | Perplexity | Helped |
| Decrease tau0 to allow faster-changing global topics (ablation) | SDM · Wikipedia | Perplexity | No clear effect |
| Increase tau1, tightening local-topic variability around global topics (ablation) | SDM · EJC | Number of topics | Helped |
Closest papers by meaning
- Conic Scan-and-Cover algorithms for nonparametric topic modeling (NeurIPS 2017 · similarity 0.89)
- Geometric Dirichlet Means Algorithm for topic inference (NeurIPS 2016 · similarity 0.88)
- Neural Dynamic Focused Topic Model (AAAI 2023 · similarity 0.88)
- Dynamic Topic Models for Temporal Document Networks (ICML 2022 · similarity 0.87)
- Discovering Discrete Latent Topics with Neural Variational Inference (ICML 2017 · similarity 0.87)
- On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling (AAAI 2024 · similarity 0.84)
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