2 papers
cs.CL2026
Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model
Thang Dang, Akira Nakagawa, Kenichi Kobayashi +1
Tokenization-free hierarchical models are emerging as a promising alternative to traditional Large Language Models (LLMs), addressing inherent preprocessing issues such as vocabula…
stat.ML2022
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing
Yuma Ichikawa, Akira Nakagawa, Hiromoto Masayuki +1
Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods using a machine learning model. With latent generative models,…