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stat.ML2024
Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback--Leibler Divergence Learning
Yuichi Ishida, Yuma Ichikawa, Aki Dote +2
We propose ratio divergence (RD) learning for discrete energy-based models, a method that utilizes both training data and a tractable target energy function. We apply RD learning t…
stat.ML2024
Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems
Yuma Ichikawa, Hiroaki Iwashita
Finding the optimal solution is often the primary goal in combinatorial optimization (CO). However, real-world applications frequently require diverse solutions rather than a singl…