Toward Unlimited Self-Learning Monte Carlo with Annealing Process Using VAE's Implicit Isometricity. - Yuma Ichikawa; Akira Nakagawa; Hiromoto Masayuki; Yuhei Umeda (CoRR, 2022)

Dataset Size Dependence of Rate-Distortion Curve and Threshold of Posterior Collapse in Linear VAE. - Yuma Ichikawa; Koji Hukushima (CoRR, 2023)

Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing. - Yuma Ichikawa; Koji Hukushima (AISTATS, 2024)

Adaptive Flip Graph Algorithm for Matrix Multiplication. - Yamato Arai; Yuma Ichikawa; Koji Hukushima (ISSAC, 2024)

Controlling Continuous Relaxation for Combinatorial Optimization. - Yuma Ichikawa (NeurIPS, 2024)

Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems. - Yuma Ichikawa; Hiroaki Iwashita (CoRR, 2024)

Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models. - Nobuo Namura; Yuma Ichikawa (CoRR, 2024)

Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback-Leibler Divergence Learning. - Yuichi Ishida; Yuma Ichikawa; Aki Dote; Toshiyuki Miyazawa; Koji Hukushima (CoRR, 2024)

Statistical Mechanics of Min-Max Problems. - Yuma Ichikawa; Koji Hukushima (Trans. Mach. Learn. Res., 2024)

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling. - Yuma Ichikawa; Yamato Arai (ICLR, 2025)

Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization. - Yamato Arai; Yuma Ichikawa (CoRR, 2025)