Due to the fact that it is expensive and time-consuming to annotate a large amount of data,
the available labeled data to train a deep neural network is usually scarce, resulting in the …

Drug-target affinity (DTA) prediction is a critical early-stage task in drug discovery. Recently,
deep learning has demonstrated remarkable efficacy in DTA prediction. However, acquiring …

Active learning enables the efficient construction of a labeled dataset by labeling informative
samples from an unlabeled dataset. In a real-world active learning scenario, the use of …

Active learning seeks to achieve strong performance with fewer training samples. It does this
by iteratively asking an oracle to label newly selected samples in a human-in-the-loop …

Despite the substantial progress of active learning for image recognition, there lacks a
systematic investigation of instance-level active learning for object detection. In this paper …

High-quality internal experimental materials data are often “small data.” Using external
datasets, eg, data from the literature and other groups, to expand the “small data” to …

Active learning (AL), allowing to query the labels by an oracle, can dwindle the
generalization error from a few labeled samples, thus well-motivating in the scenario where …

While deep neural networks play significant roles in many research areas, they are also
prone to overfitting problems under limited data instances. To overcome overfitting, this …

Graph-level classification is a critical problem in social analysis and bioinformatics. Since
annotated labels are typically costly, we intend to study this challenging task in semi …

Learning discriminative and robust representations is important for facial expression
recognition (FER) due to subtly different emotional faces and their subjective annotations …