no code implementations • 25 Mar 2024 • Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida
Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials.
no code implementations • 20 Feb 2024 • Ryo Yoshida, Taiga Someya, Yohei Oseki
Large Language Models (LLMs) have achieved remarkable success thanks to scalability on large text corpora, but have some drawback in training efficiency.
no code implementations • 19 Feb 2024 • Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe, Timothy Baldwin
This also showcases the advantage of cognitively-motivated LMs, which are typically employed in cognitive modeling, in the computational simulation of language universals.
1 code implementation • 3 May 2023 • Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Ryo Yoshida
Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface defined on the space of the atomic configurations.
1 code implementation • 24 Oct 2022 • Ryo Yoshida, Yohei Oseki
In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism.
1 code implementation • NeurIPS 2023 • Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida
Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce.
no code implementations • 1 Mar 2022 • Qi Zhang, Chang Liu, Stephen Wu, Ryo Yoshida
The design variables consist of a set of reactants in a reaction network and its network topology.
1 code implementation • 26 Jan 2022 • Minoru Kusaba, Chang Liu, Ryo Yoshida
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics.
2 code implementations • EMNLP 2021 • Ryo Yoshida, Hiroshi Noji, Yohei Oseki
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like.
1 code implementation • ACL 2021 • Tatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida, Masayuki Asahara, Kentaro Inui
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
no code implementations • 23 Jun 2020 • Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression.
1 code implementation • 6 Mar 2020 • Zhongliang Guo, Stephen Wu, Mitsuru Ohno, Ryo Yoshida
The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space.
no code implementations • 23 Dec 2019 • Minoru Kusaba, Chang Liu, Yukinori Koyama, Kiyoyuki Terakura, Ryo Yoshida
In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev.
no code implementations • 26 Aug 2015 • Osamu Hirose, Shotaro Kawaguchi, Terumasa Tokunaga, Yu Toyoshima, Takayuki Teramoto, Sayuri Kuge, Takeshi Ishihara, Yuichi Iino, Ryo Yoshida
Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells based only on shape and size, (iii) the number of imaged cells ranges in several hundreds, (iv) moves of nearly-located cells are strongly correlated and (v) cells do not divide.