Research map / Learning theory and algorithms / Optimal transport
Neural Topic Model via Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use NSTM instead of ProdLDA | 20NG document representations · 20NG | top-Purity: 0.417$\pm$0.004 -> 0.477$\pm$0.011 | Helped |
| use NSTM instead of LDA | WS document representations · WS | top-Purity: 0.446$\pm$0.022 -> 0.451$\pm$0.009 | Helped |
| use NSTM instead of ETM | TMN document representations · TMN | top-Purity: 0.556$\pm$0.022 -> 0.637$\pm$0.010 | Helped |
| use NSTM instead of ProdLDA | 20NG document representations · 20NG | top-NMI: 0.321$\pm$0.004 -> 0.415$\pm$0.012 | Helped |
| use NSTM instead of LDA | WS document representations · WS | top-NMI: 0.185$\pm$0.013 -> 0.201$\pm$0.004 | Helped |
| use NSTM instead of ETM | TMN document representations · TMN | top-NMI: 0.328$\pm$0.010 -> 0.334$\pm$0.004 | No clear effect |
| combine Sinkhorn distance with expected log-likelihood (ablation) | NSTM topic quality and document representations · 20NG | TC, TD, top-Purity, top-NMI, km-Purity, km-NMI | Mixed |
Closest papers by meaning
- OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling (NeurIPS 2020 · similarity 0.89)
- Neural Dynamic Focused Topic Model (AAAI 2023 · similarity 0.88)
- A Discrete Variational Recurrent Topic Model without the Reparametrization Trick (NeurIPS 2020 · similarity 0.87)
- FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model (NeurIPS 2024 · similarity 0.87)
- Topic Modeling as Multi-Objective Contrastive Optimization (ICLR 2024 · similarity 0.85)
- Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe (NeurIPS 2024 · similarity 0.85)
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