Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Chun-Ta Lu"'
Autor:
Chun-Sung Ferng, Allan Heydon, Arjun Gopalan, Yicheng Fan, Chun-Ta Lu, George Yu, Philip Pham, Cesar Ilharco Magalhaes, Da-Cheng Juan, Yueqi Wang
Publikováno v:
WSDM
We present Neural Structured Learning (NSL) in TensorFlow [1], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3255c9572e8617a20eb4747d2fec13ef
https://doi.org/10.1145/3437963.3441666
https://doi.org/10.1145/3437963.3441666
Autor:
Allan Heydon, Benjamin Ricaud, Linjun Shou, Dmitry Ustalov, Yanick Schraner, George Yu, Daria Baidakova, Ly Dinh, Paul Groth, Mehrnoosh Sameki, Marinka Zitnik, Flavian Vasile, Krishnaram Kenthapadi, Benjamin Wollmer, Felix Gessert, Da-Cheng Juan, Hong Cheng, Javier Albert, David Rohde, Onur Celebi, Robert West, Xiang Wang, Dawei Yin, Amine Benhalloum, Junzhou Huang, Fuchun Sun, Michaël Defferrard, Ming Gong, Rezvaneh Rezapour, Levan Tsinadze, Shubhanshu Mishra, Stratis Ioannidis, Francisco M. Couto, Yicheng Fan, Xiangnan He, Christian Scheller, Yueqi Wang, Yu Rong, Pasquale Lisena, Sharada P. Mohanty, Nicolas Aspert, Irene Teinemaa, Chun-Ta Lu, Volodymyr Miz, Jiawei Chen, Johny Jose, Xiangyu Zhao, Philip Pham, Yatao Bian, Manuel K. Schneider, Jennifer G. Dy, Nashlie Sephus, Dmitri Goldenberg, Jiliang Tang, Fuli Feng, Wenbing Huang, Olivier Jeunen, Wenqi Fan, Nikita Popov, Mario Koenig, Shobeir Fakhraei, Olesia Altunina, Smriti Bhagat, Samin Aref, Chun-Sung Ferng, Wolfram Wingerath, Evann Courdier, Martin Müller, Xiubo Geng, Xingjie Zhou, Otmane Sakhi, Dragan Cvetinovic, Florian Laurent, Norbert Ritter, Cesar Ilharco Magalhaes, Stephan Succo, Jian Pei, Ben Packer, Tingyang Xu, Ilkay Yildiz, Rose Howell, Jana Diesner, Tudor Mihai Avram, Arjun Gopalan, Alexey Drutsa, Daxin Jiang, Albert Meroño-Peñuela, Christos Faloutsos
Publikováno v:
The Web Conference 2021: companion of the World Wide Web Conference WWW 2021: April 19-23, 2021, Ljubljana, Slovenia
The Web Conference 2021
30th World Wide Web (WWW) Conference (WebConf), APR 19-23, 2021, ELECTR NETWORK
WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021)
The Web Conference 2021
30th World Wide Web (WWW) Conference (WebConf), APR 19-23, 2021, ELECTR NETWORK
WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021)
This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8db21f1ce6612200bda91f82be15d8cf
https://doi.org/10.1145/3442442.3453701
https://doi.org/10.1145/3442442.3453701
Autor:
Cesar Ilharco Magalhaes, Philip Pham, Arjun Gopalan, Allan Heydon, George Yu, Da-Cheng Juan, Chun-Ta Lu, Chun-Sung Ferng
Publikováno v:
KDD
We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c23adf6006a192cebd5078f29999cd3
https://doi.org/10.1145/3394486.3406701
https://doi.org/10.1145/3394486.3406701
Autor:
Chun-Ta Lu, Sujith Ravi, Tom Duerig, Gao Yaxi, Da-Cheng Juan, Andrew Tomkins, Zhen Li, Yi-Ting Chen, Aleksei Timofeev, Futang Peng
Publikováno v:
WSDM
"How to learn image embeddings that capture fine-grained semantics based on the instance of an image?" "Is it possible for such embeddings to further understand image semantics closer to humans' perception?" In this paper, we present, Graph-Regulariz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1529678236fdf3e3c1cb8348e9e7af2
https://doi.org/10.1145/3336191.3371784
https://doi.org/10.1145/3336191.3371784
Publikováno v:
COLING
Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. However, with the growing size and granularity of the entity types, rare researches in previous concern with newly emerged ent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a74dd87c2f7180b149a8519862616ece
https://doi.org/10.18653/v1/2020.coling-main.7
https://doi.org/10.18653/v1/2020.coling-main.7
Publikováno v:
IEEE BigData
Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications. Structural brain network (e.g. DTI) and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b13c6cb7392684d0b0091a57a86ba939
http://arxiv.org/abs/1911.03583
http://arxiv.org/abs/1911.03583
Autor:
Manan Shah, Zhen Li, Chen Sun, Ariel Fuxman, Chao Jia, Krishnamurthy Viswanathan, Aleksei Timofeev, Chun-Ta Lu
Publikováno v:
CIKM
Image classification models take image pixels as input and predict labels in a predefined taxonomy. While contextual information (e.g. text surrounding an image) can provide valuable orthogonal signals to improve classification, the typical setting i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::add4f5376de146bd9d83fe80ec5ff634
https://doi.org/10.1145/3357384.3357987
https://doi.org/10.1145/3357384.3357987
Autor:
Lifang He, Chaozhuo Li, Huang He, Lei Zheng, Philip S. Yu, Chun-Ta Lu, Vahid Noroozi, Sihong Xie, Bowen Dong
Publikováno v:
DSAA
In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users' various interests, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b1f01111b9d50f4c185140561bda47c
https://doi.org/10.1109/dsaa.2019.00015
https://doi.org/10.1109/dsaa.2019.00015
Publikováno v:
SIGIR
Exploiting historical data of users to make future predictions lives at the heart of building effective recommender systems (RS). Recent approaches for sequential recommendations often render past actions of a user into a sequence, seeking to capture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b5abb0e55feb2ef2fa769b727021925
https://doi.org/10.1145/3331184.3331329
https://doi.org/10.1145/3331184.3331329
Publikováno v:
SIGIR
Existing recommendation methods mostly learn fixed vectors for users and items in a low-dimensional continuous space, and then calculate the popular dot-product to derive user-item distances. However, these methods suffer from two drawbacks: (1) the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0c13b620961a397925a371d9f28ad62
https://doi.org/10.1145/3331184.3331330
https://doi.org/10.1145/3331184.3331330