GitHub

A comprehensive implementation of self-supervised reinforcement learning algorithms including contrastive learning, predictive models, and representation learning for RL tasks.

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...
Scientific Research Publishing

Xu, D., Ruan, C.W., Korpeoglu, E., Kumar, S. and Achan, K. (2020) Inductive Representation Learning on Temporal Graphs. arXiv: 2002.07962.

ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...
ascopubs.org

Knowledge Representation of a Multicenter Adolescent and Young Adult Cancer Infrastructure: Development of the STRONG AYA Knowledge Graph

We structured the STRONG AYA case-mix and core outcome measures concepts and their properties as knowledge graphs. Having identified the corresponding standard terminologies, we developed a semantic ...
Frontiers

SSEL: spike-based structural entropic learning for spiking graph neural networks

Spiking Neural Networks (SNNs) offer transformative, event-driven neuromorphic computing with unparalleled energy efficiency, representing a third-generation AI paradigm. Extending this paradigm to ...
IEEE

scGCRC: Graph and Contrastive-Based Representation Learning for Single-Cell RNA-Seq Data Clustering

Abstract: The advent of single-cell RNA-sequencing (scRNA-seq) technology promotes biological analysis at the cellular level. Clustering cells to identify the type of cell is an important step in ...
IEEE

Graph Representation Learning Combining Denoising and Masked Reconstruction With Contrastive Learning

Abstract: In the field of graph self-supervised learning (GSSL), graph autoencoders and graph contrastive learning are two mainstream methods. Graph autoencoders aim to learn representations by ...
GitHub

GRACE: Generative Representation Learning via Contrastive Policy Optimization

@misc{sun2025gracegenerativerepresentationlearning, title={GRACE: Generative Representation Learning via Contrastive Policy Optimization}, author={Jiashuo Sun and ...
eLife

Neural Representation of Associative Threat Learning in Pulvinar Divisions, Lateral Geniculate Nucleus, and Mediodorsal Thalamus in Humans

The study provides valuable insights into the role of thalamic nuclei in associative threat and extinction learning, supported by a large dataset and multipronged analyses. However, aspects of the ...
EurekAlert!

Counterfactual learning on graphs: A survey

Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...