Search Results for author: Yulun Wu

Found 5 papers, 2 papers with code

Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance

no code implementations19 Sep 2022 Dichucheng Li, Yulun Wu, Qinyu Li, Jiahao Zhao, Yi Yu, Fan Xia, Wei Li

Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions.

Variational Causal Inference

no code implementations13 Sep 2022 Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, George Karypis

Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.

Causal Inference

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

4 code implementations ICLR 2022 Yulun Wu, Mikaela Cashman, Nicholas Choma, Érica T. Prates, Verónica G. Melesse Vergara, Manesh Shah, Andrew Chen, Austin Clyde, Thomas S. Brettin, Wibe A. de Jong, Neeraj Kumar, Martha S. Head, Rick L. Stevens, Peter Nugent, Daniel A. Jacobson, James B. Brown

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.

Drug Discovery Graph Attention

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

no code implementations14 Feb 2019 Yanpeng Cao, Dayan Guan, Yulun Wu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).

Autonomous Driving Pedestrian Detection

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