no code implementations • 3 Sep 2024 • Shutong Niu, Ruoyu Wang, Jun Du, Gaobin Yang, Yanhui Tu, Siyuan Wu, Shuangqing Qian, Huaxin Wu, Haitao Xu, Xueyang Zhang, Guolong Zhong, Xindi Yu, Jieru Chen, Mengzhi Wang, Di Cai, Tian Gao, Genshun Wan, Feng Ma, Jia Pan, Jianqing Gao
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge.
1 code implementation • 12 Apr 2024 • Yujie Li, Yanbin Wang, Haitao Xu, Bin Liu, Jianguo Sun, Zhenhao Guo, Wenrui Ma
Unlike data-driven classifiers, TMDC, guided by Bayesian principles, utilizes the conditional likelihood from diffusion models to determine the class probabilities of input images, thereby insulating against the influences of data shift and the limitations of adversarial training.
1 code implementation • 18 Feb 2024 • Yujie Li, Yanbin Wang, Haitao Xu, Zhenhao Guo, Zheng Cao, Lun Zhang
To address this gap, this paper introduces URLBERT, the first pre-trained representation learning model applied to a variety of URL classification or detection tasks.
no code implementations • 30 Oct 2023 • Haitao Xu, Songwei Liu, Yuyang Xu, Shuai Wang, Jiashi Li, Chenqian Yan, Liangqiang Li, Lean Fu, Xin Pan, Fangmin Chen
Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning.
no code implementations • 16 Jan 2020 • Haitao Xu, Brendan McCane, Lech Szymanski, Craig Atkinson
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn.
no code implementations • 31 Oct 2019 • Haitao Xu, Brendan McCane, Lech Szymanski
Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL).