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cs.LG2026
Simulation-free and finite-time diffusion model
Kentaro Kaba, Masayuki Ohzeki, Yuki Sughiyama
The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distributions. Conventional approac…
cs.LG2025
Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning
Yasushi Hasegawa, Masayuki Ohzeki
We compare Ising ({-1, +1}) and QUBO ({0, 1}) encodings for Boltzmann machine learning under controlled protocols that fix the sampler, optimizer, and learning-rate design within e…
cs.LG2024
Schödinger Bridge Type Diffusion Models as an Extension of Variational Autoencoders
Kentaro Kaba, Reo Shimizu, Masayuki Ohzeki +1
Generative diffusion models use time-forward and backward stochastic differential equations to connect the data and prior distributions. While conventional diffusion models (e.g.,…