Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Heng-Tze Cheng"'
Autor:
Furkan Kocayusufoglu, Tao Wu, Anima Singh, Georgios Roumpos, Heng-Tze Cheng, Sagar Jain, Ed Chi, Ambuj Singh
Publikováno v:
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining.
Autor:
Jon Effrat, Ayooluwakunmi Jeje, Moustafa Alzantot, Heng-Tze Cheng, Tameen Khan, Tushar Deepak Chandra, Ellie Ka-In Chio, Ajit Apte, Tarush Bali, Dima Kuzmin, Santiago Ontañón, Sukhdeep Sodhi, Allen Wu, Amol Wankhede, Senqiang Zhou, Harry Fung, Ankit Kumar, Ambarish Jash, Sarvjeet Singh, Pei Cao, Nitin Jindal
Publikováno v:
KDD
As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abe885d12d0e1e2c88deda719a347baf
Autor:
Wei Wang, Pei Cao, Tao Wu, Jyun-Yu Jiang, Nitin Jindal, Georgios Roumpos, Harish Ganapathy, Xinyang Yi, Heng-Tze Cheng, Ed H. Chi
Publikováno v:
WWW
Modern online content-sharing platforms host billions of items like music, videos, and products uploaded by various providers for users to discover items of their interests. To satisfy the information needs, the task of effective item retrieval (or i
Autor:
Xiang Ma, Li Zhang, Tao Wu, Heng-Tze Cheng, Ritesh Agarwal, Yu Du, Steffen Rendle, Ankit Kumar, John Anderson, Sarvjeet Singh, Ed H. Chi, Ellie Ka-In Chio, Wen Li, Alex Soares, Pei Cao, Nitin Jindal, Dima Kuzmin, Tushar Deepak Chandra
Publikováno v:
CIKM
Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa815879e4f2196fc8b175ff3dfa5177
Autor:
Ritesh Agarwal, Craig Boutilier, Sanmit Narvekar, Eugene Ie, Jing Wang, Rui Wu, Vihan Jain, Tushar Deepak Chandra, Heng-Tze Cheng
Publikováno v:
IJCAI
Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are re
Autor:
Feng-Tso Sun, Kuo, Cynthia, Heng-Tze Cheng, Senaka Buthpitiya, Collins, Patricia, Griss, Martin L
"Continuous stress monitoring may help users better understand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were limited to a laboratory environment where parti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::976d24c2786a16c1e2aa781a365648dc
Autor:
Senaka Buthpitiya, Patrick Tague, Feng-Tso Sun, Anind K. Dey, Martin L. Griss, Heng-Tze Cheng
Sharing sensitive context information among multiple distributed components in mobile environments introduces major security concerns. The distributed sensing, processing and actuating components of these applications can be compromised and modified
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccb084ccbf1e65367bf183afcf01eed0
Autor:
Heng-Tze Cheng
The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide acti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05b76efc2eaa21059bd40775d2779532
Autor:
Martin Wicke, Jamie Smith, Cassandra Xia, Georgios Roumpos, Philipp Tucker, David A W Soergel, Clemens Mewald, Mustafa Ispir, Jianwei Xie, Illia Polosukhin, Lichan Hong, D. Sculley, Yuan Tang, Heng-Tze Cheng, Zakaria Haque
Publikováno v:
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast
Autor:
Zakaria Haque, Hrishi Aradhye, Greg S. Corrado, Jeremiah Harmsen, Vihan Jain, Xiaobing Liu, Tushar Deepak Chandra, Mustafa Ispir, Glen Anderson, Wei Chai, Lichan Hong, Hemal Shah, Levent Koc, Rohan Anil, Tal Shaked, Heng-Tze Cheng
Publikováno v:
DLRS@RecSys
Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformatio