3 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…
cs.LG2024
Direct Quantized Training of Language Models with Stochastic Rounding
Kaiyan Zhao, Tsuguchika Tabaru, Kenichi Kobayashi +3
Although recent quantized Large Language Models (LLMs), such as BitNet, have paved the way for significant reduction in memory usage during deployment with binary or ternary weight…
cs.LG2021
Neural Network Module Decomposition and Recomposition
Hiroaki Kingetsu, Kenichi Kobayashi, Taiji Suzuki
We propose a modularization method that decomposes a deep neural network (DNN) into small modules from a functionality perspective and recomposes them into a new model for some oth…