4 citations · 21 across the 14 of their papers we have counts for
3 papers · 1 filter
Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses
Han Bao, Shinsaku Sakaue, Yuki Takezawa
The gradient descent (GD) has been one of the most common optimizer in machine learning. In particular, the loss landscape of a neural network is typically sharpened during the ini…
An Empirical Study of Self-supervised Learning with Wasserstein Distance
Makoto Yamada, Yuki Takezawa, Guillaume Houry +4
In this study, we delve into the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where…
Approximating 1-Wasserstein Distance with Trees
Makoto Yamada, Yuki Takezawa, Ryoma Sato +3
Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applicatio…