Tianfan Fu
Georgia Institute of Technology
43 Papers
192 Citations
Tianfan Fu is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 11, co-authored 30 publications. Previous affiliations of Tianfan Fu include Shanghai Jiao Tong University.
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Papers
Scientific discovery in the age of artificial intelligence
Hanchen Wang,Tianfan Fu,Yuanqi Du,Wenhao Gao,Kexin Huang,Ziming Liu,Payal Chandak,Shengchao Liu,Peter Van Katwyk,A Deac,Animashree Anandkumar,Karianne J. Bergen,Carla Gomes,Shirley Ho,Pushmeet Kohli,L. Lasenby,Jure Leskovec,Tie-Yan Liu,Arjun K. Manrai,Debora Marks,Bharath Ramsundar,Le Song,Jimeng Sun,Jian Tang,Petar Veličković,Max Welling,Linfeng Zhang,Connor W. Coley,Yoshua Bengio,Marinka Zitnik +29 more
TL;DR: This work examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deeplearning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
696
DeepPurpose: a deep learning library for drug-target interaction prediction.
TL;DR: DeepPurpose as discussed by the authors is a comprehensive and easy-to-use DL library for drug-target interaction prediction, which supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures.
368
Deep feature for text-dependent speaker verification
TL;DR: Experiments showed that deep feature based methods can obtain significant performance improvements compared to the traditional baselines, no matter if they are directly applied in the GMM-UBM system or utilized as identity vectors.
215
Artificial intelligence foundation for therapeutic science
Kexin Huang,Tianfan Fu,Wenhao Gao,Yue Zhao,Yusuf H. Roohani,Jure Leskovec,Connor W. Coley,Cao Xiao,Jimeng Sun,Marinka Zitnik +9 more
TL;DR: The Therapeutics Data Commons as discussed by the authors is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.
132
•Proceedings Article
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Kexin Huang,Tianfan Fu,Wenhao Gao,Yue Zhao,Yusuf H. Roohani,Jure Leskovec,Connor W. Coley,Cao Xiao,Jimeng Sun,Marinka Zitnik +9 more
- 18 Feb 2021
TL;DR: The Therapeutics Data Commons (TDC) as mentioned in this paper is an open-science platform to systematically access and evaluate machine learning across the entire range of therapeutics and includes 66 AI-ready datasets spread across 22 learning tasks.