3 citations · 5 across the 6 of their papers we have counts for
6 papers
DegradeQuery: Counterfactual Tuple Pretraining for Context-Aware PROTAC Degradation Prediction
Dong Xu, Zhangfan Yang, Jiantao Wu +3
Proteolysis-targeting chimeras (PROTACs) induce protein degradation by recruiting a target protein to an E3 ubiquitin ligase, making degradation a joint outcome of the degrader mol…
Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning
Dong Xu, Jiantao Wu, Qihua Pan +3
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing…
Rethinking Positive Pairs in Contrastive Learning
Jiantao Wu, Sara Atito, Zhenhua Feng +3
The training methods in AI do involve semantically distinct pairs of samples. However, their role typically is to enhance the between class separability. The actual notion of simil…
Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning
Jiantao Wu, Shentong Mo, Sara Atito +3
Recently, self-supervised metric learning has raised attention for the potential to learn a generic distance function. It overcomes the limitations of conventional supervised one,…
Object-wise Masked Autoencoders for Fast Pre-training
Jiantao Wu, Shentong Mo
Self-supervised pre-training for images without labels has recently achieved promising performance in image classification. The success of transformer-based methods, ViT and MAE, d…
Disentangling Action Sequences: Discovering Correlated Samples
Jiantao Wu, Lin Wang
Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the perform…