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 • 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 • 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 • 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.