no code implementations • IWSLT (EMNLP) 2018 • Nguyen Bach, Hongjie Chen, Kai Fan, Cheung-Chi Leung, Bo Li, Chongjia Ni, Rong Tong, Pei Zhang, Boxing Chen, Bin Ma, Fei Huang
This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.
no code implementations • 18 Mar 2024 • Hongjie Chen, Jingqiu Ding, Tommaso d'Orsi, Yiding Hua, Chih-Hung Liu, David Steurer
We develop the first pure node-differentially-private algorithms for learning stochastic block models and for graphon estimation with polynomial running time for any constant number of blocks.
no code implementations • 19 Oct 2022 • Xu Yuan, Chen Xu, Qiwei Chen, Tao Zhuang, Hongjie Chen, Chao Li, Junfeng Ge
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
no code implementations • 16 Jun 2022 • Hongjie Chen, Tommaso d'Orsi
In this paper, we show that there exists a family of design matrices lacking well-spreadness such that consistent recovery of the parameter vector in the above robust linear regression model is information-theoretically impossible.
no code implementations • 14 Oct 2020 • Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Sungchul Kim, Hoda Eldardiry
GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model.
no code implementations • 25 Sep 2020 • Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Hoda Eldardiry
We propose a novel context integrated relational model, Context Integrated Graph Neural Network (CIGNN), which leverages the temporal, relational, spatial, and dynamic contextual dependencies for multi-step ahead demand forecasting.
no code implementations • 10 Jun 2018 • Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li
We also find that it is important to have sufficient speech segment pairs to train the deep CNN for effective acoustic word embeddings.
no code implementations • MediaEval 2015 Workshop 2015 • Jingyong Hou, Van Tung Pham, Cheung-Chi Leung, Lei Wang, HaiHua Xu, Hang Lv, Lei Xie, Zhonghua Fu, Chongjia Ni, Xiong Xiao, Hongjie Chen, Shaofei Zhang, Sining Sun, Yougen Yuan, Pengcheng Li, Tin Lay Nwe, Sunil Sivadas, Bin Ma, Eng Siong Chng, Haizhou Li
This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation.
Ranked #9 on Keyword Spotting on QUESST
no code implementations • 16 Oct 2014 • Peng Yang, HaiHua Xu, Xiong Xiao, Lei Xie, Cheung-Chi Leung, Hongjie Chen, JIA YU, Hang Lv, Lei Wang, Su Jun Leow, Bin Ma, Eng Siong Chng, Haizhou Li
For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3.
Ranked #6 on Keyword Spotting on QUESST