Summary of Gradient-based Discrete Sampling with Automatic Cyclical Scheduling, by Patrick Pynadath et al.
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
by Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, Ruqi Zhang
First submitted to arxiv on: 27 Feb 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 Our paper proposes an automatic cyclical scheduling approach to efficiently and accurately sample multimodal discrete distributions, a common challenge in high-dimensional deep models. The method consists of three components: a cyclical step size schedule for discovering new modes and exploiting each mode, a balancing schedule ensuring efficient Markov chain proposals, and an automatic tuning scheme for hyperparameter adjustment across diverse datasets. We prove non-asymptotic convergence and inference guarantee for our method in general discrete distributions. Experimental results show the superiority of our approach in sampling complex multimodal discrete distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find different shapes within a big pile of puzzle pieces. Sometimes, it’s hard to get out of one shape and explore other options because we’re stuck on a certain path. In computer science, this is similar to what happens when trying to find different patterns in complex data. Our solution uses a special scheduling system that helps us jump between these patterns more efficiently and accurately. We’ve tested our approach on many datasets and shown it’s better than previous methods at finding these patterns. |
Keywords
* Artificial intelligence * Hyperparameter * Inference
