no code implementations • 5 May 2024 • Zhendong Chu, Zichao Wang, Ruiyi Zhang, Yangfeng Ji, Hongning Wang, Tong Sun
Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks.
no code implementations • 25 Oct 2023 • Zhendong Chu, Ruiyi Zhang, Tong Yu, Rajiv Jain, Vlad I Morariu, Jiuxiang Gu, Ani Nenkova
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate.
no code implementations • 15 Feb 2023 • Zhendong Chu, Hongning Wang
In this paper, we explore the structured heterogeneity among tasks via clustering to improve meta-RL.
no code implementations • 6 Nov 2022 • Ye Gao, Zhendong Chu, Hongning Wang, John Stankovic
We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain.
1 code implementation • 24 May 2022 • Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu
We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.
no code implementations • 22 Jul 2021 • Zhendong Chu, Hongning Wang
This creates a sparsity issue and limits the quality of machine learning models trained on such data.
2 code implementations • 24 Dec 2020 • Zhendong Chu, Jing Ma, Hongning Wang
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost.
Ranked #1 on Image Classification on LabelMe
no code implementations • 17 Dec 2020 • Zhendong Chu, Haiyun Jiang, Yanghua Xiao, Wei Wang
We see information sources as multiple views and fusing them to construct an intact space with sufficient information.
no code implementations • 27 Feb 2019 • Jindong Chen, Ao Wang, Jiangjie Chen, Yanghua Xiao, Zhendong Chu, Jingping Liu, Jiaqing Liang, Wei Wang
Taxonomies play an important role in machine intelligence.