Summary of Denoising Fisher Training For Neural Implicit Samplers, by Weijian Luo and Wei Deng
Denoising Fisher Training For Neural Implicit Samplers
by Weijian Luo, Wei Deng
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computation (stat.CO)
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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 introduces Denoising Fisher Training (DFT), a novel training approach for neural implicit samplers that tackles issues such as poor mode covering behavior, unstable training dynamics, and sub-optimal performances. The approach frames the training problem as minimizing the Fisher divergence by deriving a tractable loss function. DFT is empirically validated across diverse sampling benchmarks, including synthetic distributions, Bayesian logistic regression, and high-dimensional energy-based models (EBMs). Notably, in experiments with high-dimensional EBMs, the best one-step DFT neural sampler achieves results comparable to MCMC methods using up to 200 sampling steps, demonstrating a substantially greater efficiency over 100 times higher. This approach has implications for efficient sampling methodologies across broader applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to sample from certain types of distributions in machine learning and science. Right now, some existing methods don’t work well because they can get stuck or don’t give good results. The authors come up with a new way to train these samplers that is more efficient and gives better results. They test their approach on different kinds of problems and show it works just as well as other methods that take much longer. |
Keywords
* Artificial intelligence * Logistic regression * Loss function * Machine learning
