8 citations · 16 across the 19 of their papers we have counts for
5 papers · 1 filter
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
Yuma Ichikawa, Yamato Arai
Learning-based methods have gained attention as general-purpose solvers due to their ability to automatically learn problem-specific heuristics, reducing the need for manually craf…
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…
Statistical Mechanics of Min-Max Problems
Yuma Ichikawa, Koji Hukushima
Min-max optimization problems, also known as saddle point problems, have attracted significant attention due to their applications in various fields, such as fair beamforming, gene…
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models
Nobuo Namura, Yuma Ichikawa
Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data. However, these models often suffer fro…
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…