Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
i-SCOOP

Context graph

Discover what context graphs are, why they're revolutionizing AI systems, and who's building this trillion-dollar technology ...
Communications of the ACM

Engineering Agentic Context for Trustworthy Clinical Autonomy

CARE-ACE supports autonomy through bounded agentic reasoning, in which diagnostic, prognostic, planning, and risk-assessment ...
Scientific Research Publishing

Hamilton, W.L. (2020) Graph Representation Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14, 1-159.

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
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 ...
Scientific Research Publishing

Hamilton, W.L. (2020) Graph Representation Learning. Morgan & Claypool Publishers.

ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...
C&EN

MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. In response to the aforementioned challenges mentioned above, ...