Abstract: Existing deep clustering methods leverage contrastive or non-contrastive learning to facilitate downstream tasks. Most contrastive-based methods typically learn representations by comparing ...

Abstract: Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that GDA is an ...

This repository provides a comprehensive implementation of contrastive learning approaches for multi-label classification tasks. Our work explores the effectiveness of contrastive learning across ...

Agents that operate autonomously benefit from lifelong learning capabilities. However, compatible training algorithms must comply with the decentralized nature of these systems which imposes ...

1 German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany 2 Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany Introduction: Neurodegenerative ...

Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning ...

1 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China 2 i-Large Model Innovation Lab of Ideological and Political Science, University of Electronic ...