In the field of skeleton-based action recognition, current top-performing graph convolutional
networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature …

Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive
learning (CL) learns a useful representation function by pulling positive samples close to …

Collaborative Metric Learning (CML) has recently emerged as a popular method in
recommendation systems (RS), closing the gap between metric learning and collaborative …

In-Batch contrastive learning is a state-of-the-art self-supervised method that brings
semantically-similar instances close while pushing dissimilar instances apart within a mini …

Vision-language pre-trained models have exhibited significant advancements in various
multimodal and unimodal tasks in recent years, including cross-modal recipe retrieval …

In the field of chemical synthesis planning, the accurate recommendation of reaction
conditions is essential for achieving successful outcomes. This work introduces an …

Human Activity Recognition (HAR) systems have been extensively studied by the vision and
ubiquitous computing communities due to their practical applications in daily life, such as …

Spurious correlations can cause strong biases in deep neural networks impairing
generalization ability. While most existing debiasing methods require full supervision on …

Contrastive learning has recently shown great potential to improve text-based knowledge
graph completion (KGC). In this paper, we propose to learn a more semantically structured …

Due to the recent growing data size of deep learning workloads, loading data from storage is
increasingly becoming a performance bottleneck for neural network systems. In this article …