1 code implementation • 18 Dec 2023 • Zhi Jin, Sheng Xu, Xiang Zhang, Tianze Ling, Nanqing Dong, Wanli Ouyang, Zhiqiang Gao, Cheng Chang, Siqi Sun
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research.
no code implementations • 10 Dec 2023 • Cheng Chang, Zhouping Xin, Tieyong Zeng
However, when the spatial dimension is one, the original curl-free relaxation component is inapplicable, and the approximation formula for dummy variables, which works well in a 2-dimensional scenario, fails to provide a reasonable output in the 1-dimensional case.
1 code implementation • 4 Dec 2023 • Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuanjing Huang, Xipeng Qiu
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique.
1 code implementation • bioRxiv 2023 • Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang
De novo peptide sequencing is a promising approach for novel peptide discovery.
1 code implementation • 11 Jul 2023 • Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
no code implementations • 13 May 2022 • Cheng Chang, Tieyong Zeng
The proposed model learns from both data and physics constraints through the training of a deep neural network, which serves as part of the covariance function in GPR.
no code implementations • 23 Oct 2020 • Yuhan Zhang, Cheng Chang
This paper models the US-China trade conflict and attempts to analyze the (optimal) strategic choices.
no code implementations • 12 Dec 2019 • Yichao Lu, Cheng Chang, Himanshu Rai, Guangwei Yu, Maksims Volkovs
We present our winning solution to the Open Images 2019 Visual Relationship challenge.
1 code implementation • NeurIPS 2019 • Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti
Despite recent progress in computer vision, image retrieval remains a challenging open problem.
no code implementations • 12 Jun 2019 • Cheng Chang, Himanshu Rai, Satya Krishna Gorti, Junwei Ma, Chundi Liu, Guangwei Yu, Maksims Volkovs
We present our solution to Landmark Image Retrieval Challenge 2019.
1 code implementation • CVPR 2019 • Cheng Chang, Guangwei Yu, Chundi Liu, Maksims Volkovs
Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps.
no code implementations • WS 2018 • Kaige Xie, Cheng Chang, Liliang Ren, Lu Chen, Kai Yu
Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data.
2 code implementations • 19 Dec 2017 • Alex Levinshtein, Cheng Chang, Edmund Phung, Irina Kezele, Wenzhangzhi Guo, Parham Aarabi
Augmented reality is an emerging technology in many application domains.
no code implementations • EMNLP 2017 • Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou, Kai Yu
The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical.
no code implementations • EMNLP 2017 • Lu Chen, Xiang Zhou, Cheng Chang, Runzhe Yang, Kai Yu
Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy.
no code implementations • EACL 2017 • Lu Chen, Runzhe Yang, Cheng Chang, Zihao Ye, Xiang Zhou, Kai Yu
On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios.