Summary of Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback–leibler Divergence Learning, by Yuichi Ishida et al.
Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback–Leibler Divergence Learning
by Yuichi Ishida, Yuma Ichikawa, Aki Dote, Toshiyuki Miyazawa, Koji Hukushima
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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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 paper proposes a new method called Ratio Divergence (RD) learning for training discrete energy-based models, which combines the strengths of forward and reverse Kullback-Leibler divergence (KLD) learning. The authors apply RD learning to Restricted Boltzmann Machines (RBMs), a minimal model that can approximate any discrete distribution. The approach effectively addresses issues with underfitting and mode-collapse in traditional KLD-based methods. Numerical experiments show that RD learning outperforms other methods in terms of energy function fitting, mode-covering, and learning stability across various models, particularly as the dimensions increase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train special kinds of computer models called energy-based models. These models are used to understand and generate complex data like images or text. The researchers developed a method called Ratio Divergence (RD) learning that helps these models learn better. They tested their approach on some simple models called Restricted Boltzmann Machines (RBMs). The results show that RD learning is much more effective than other methods in making these models work well. |
Keywords
» Artificial intelligence » Underfitting
