no code implementations • 28 Mar 2024 • Kangming Xu, Huiming Zhou, Haotian Zheng, Mingwei Zhu, Qi Xin
With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice.
1 code implementation • 23 Mar 2024 • Ruiqiang Xiao, Jiayu Huo, Haotian Zheng, Yang Liu, Sebastien Ourselin, Rachel Sparks
Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
no code implementations • 9 Mar 2024 • Chen Li, Haotian Zheng, Yiping Sun, Cangqing Wang, Liqiang Yu, Che Chang, Xinyu Tian, Bo Liu
In the realm of computational knowledge representation, Knowledge Graph Reasoning (KG-R) stands at the forefront of facilitating sophisticated inferential capabilities across multifarious domains.
no code implementations • 29 Feb 2024 • Yanlin Zhou, Kai Tan, Xinyu Shen, Zheng He, Haotian Zheng
Proteins are essential for life, and their structure determines their function.
Protein Language Model Protein Secondary Structure Prediction +1
1 code implementation • NeurIPS 2023 • Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han
To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection.
Out-of-Distribution Detection Out of Distribution (OOD) Detection +1
no code implementations • 4 Sep 2023 • Shuai Li, Haotian Zheng, Jiawei Wang, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
no code implementations • 24 Mar 2022 • Jialin Wang, Rui Gao, Haotian Zheng, Hao Zhu, C. -J. Richard Shi
Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy.