no code implementations • 21 Mar 2024 • Rakuten Group, Aaron Levine, Connie Huang, Chenguang Wang, Eduardo Batista, Ewa Szymanska, Hongyi Ding, Hou Wei Chou, Jean-François Pessiot, Johanes Effendi, Justin Chiu, Kai Torben Ohlhus, Karan Chopra, Keiji Shinzato, Koji Murakami, Lee Xiong, Lei Chen, Maki Kubota, Maksim Tkachenko, Miroku Lee, Naoki Takahashi, Prathyusha Jwalapuram, Ryutaro Tatsushima, Saurabh Jain, Sunil Kumar Yadav, Ting Cai, Wei-Te Chen, Yandi Xia, Yuki Nakayama, Yutaka Higashiyama
We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models.
no code implementations • 13 Apr 2023 • Ting Cai, Kirthevasan Kandasamy
When the labeling cost is $B$, our algorithm, which chooses to label a point if the uncertainty is larger than a time and cost dependent threshold, achieves a worst-case upper bound of $\widetilde{O}(B^{\frac{1}{3}} K^{\frac{1}{3}} T^{\frac{2}{3}})$ on the loss after $T$ rounds.
no code implementations • 17 Aug 2021 • Penghua Zhai, Huaiwei Cong, Gangming Zhao, Chaowei Fang, Jinpeng Li, Ting Cai, Huiguang He
To avoid the subjectivity associated with these methods, we propose the MVCNet, a novel unsupervised three dimensional (3D) representation learning method working in a transformation-free manner.
1 code implementation • 10 Apr 2021 • Ting Cai, Vincent Y. F. Tan, Cédric Févotte
We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique.
no code implementations • 3 Feb 2021 • Jinpeng Li, Yaling Tao, Ting Cai
We exploit liver cancer prediction model using machine learning algorithms based on epidemiological data of over 55 thousand peoples from 2014 to the present.
1 code implementation • 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 • Jinpeng Li, Hao Chen, Ting Cai
However, most of them are iterative methods, which need considerable training time and are unfeasible in practice.