2 papers
cs.AI2026
SUNTA: Hierarchical Video Prediction with Surprise-based Chunking
Tomoshi Iiyama, Masahiro Suzuki, Yutaka Matsuo
Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, their performance hinges on how…
cs.LG2024
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…