Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
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.
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.
4 code implementations • • 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.
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).