Summary of When Predict Can Also Explain: Few-shot Prediction to Select Better Neural Latents, by Kabir Dabholkar et al.
When predict can also explain: few-shot prediction to select better neural latents
by Kabir Dabholkar, Omri Barak
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach to infer underlying neural activity dynamics using latent variable models. By revealing the limitations of traditional co-smoothing methods and introducing a new few-shot co-smoothing metric, researchers can now develop more accurate models that better reflect true dynamics. The study employs Hidden Markov Models in a student-teacher setup, demonstrating how high co-smoothing model spaces encompass arbitrary extraneous dynamics. By analyzing real neural data using LFADS and STNDT methods, the authors validate their findings and provide a novel measure to validate latent variables. This research offers significant improvements for latent dynamics inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make computer models better at understanding what’s really going on in our brains. Right now, we use special tools called latent variable models to figure out the underlying patterns of brain activity. The problem is that these models can be fooled by extra information they pick up along the way. This study shows how to fix this problem and create more accurate models. By using a new method called few-shot co-smoothing, researchers can develop better models that are closer to reality. |
Keywords
» Artificial intelligence » Few shot » Inference
