Summary of Sequential Monte Carlo For Inclusive Kl Minimization in Amortized Variational Inference, by Declan Mcnamara et al.
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
by Declan McNamara, Jackson Loper, Jeffrey Regier
First submitted to arxiv on: 15 Mar 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 This research paper proposes a new method for training encoder networks to perform amortized variational inference, which is an increasingly popular choice of variational objective. The authors aim to address the challenges associated with minimizing the inclusive Kullback-Leibler divergence, particularly the heavily biased gradients and circular pathology present in existing methods like Reweighted Wake-Sleep (RWS). To achieve this, they introduce SMC-Wake, a procedure that uses likelihood-tempered sequential Monte Carlo samplers to estimate the gradient of the inclusive KL divergence. The proposed method interleaves stochastic gradient updates, SMC samplers, and iterative improvement to an estimate of the normalizing constant to reduce bias from self-normalization. Experimental results on both simulated and real datasets demonstrate that SMC-Wake fits variational distributions that approximate the posterior more accurately than existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train machines to do something called amortized variational inference. It’s like trying to figure out what someone might be thinking, but for computers! The old way of doing it wasn’t very good because it got stuck in a loop and didn’t get the right answer. So, the researchers came up with a new method that uses special computer algorithms to help the machine learn better. They tested it on some fake data and real data, and it worked much better than the old way! This means we can use computers to make better predictions and decisions. |
Keywords
* Artificial intelligence * Encoder * Inference * Likelihood
