29 citations · 110 across the 30 of their papers we have counts for
5 papers · 1 filter
Learning Trajectories are Generalization Indicators
Jingwen Fu, Zhizheng Zhang, Dacheng Yin +2
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient des…
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Shengnan An, Zeqi Lin, Bei Chen +3
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…
Test-time Batch Normalization
Tao Yang, Shenglong Zhou, Yuwang Wang +2
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of…
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
Siyuan Liu, Yusong Wang, Tong Wang +6
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential ro…
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
Tao Yang, Xuanchi Ren, Yuwang Wang +2
Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. A well-defined theoretical gu…