Research map / Probabilistic and scientific modeling / Variational and generative sampling
On-the-fly Rectification for Robust Large-Vocabulary Topic Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ENN with LAW instead of AP with AW | Topic inference · NeurIPS, New York Times, Movies, Songs | topic quality | No clear effect |
| Use LR-JSMF instead of AP+AW | Topic inference · NeurIPS, New York Times, Movies, Songs | runtime: 1∼2 orders of magnitude | Helped |
| Use PALM+LAW instead of AP+AW | Topic inference · NeurIPS, New York Times, Movies, Songs | topic quality | Mixed |
| Increase vocabulary size | LR-JSMF · NeurIPS | relative recovery error | Helped |
| Increase vocabulary size | LR-JSMF · NeurIPS | MST-Incoherence | Helped |
| Initialize ENN from the raw word-document data using randomized eigendecomposition | LR-JSMF · NeurIPS, New York Times, Movies, Songs | topic quality | No clear effect |
Closest papers by meaning
- A Discrete Variational Recurrent Topic Model without the Reparametrization Trick (NeurIPS 2020 · similarity 0.85)
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- Provable Algorithms for Inference in Topic Models (ICML 2016 · similarity 0.85)
- Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies (ICLR 2021 · similarity 0.84)
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