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Graph-Based Multi-Agent Reinforcement Learning for Collaborative Information Dissemination

Graph-Based Multi-Agent Reinforcement Learning for Collaborative Information Dissemination

by instadatahelp | Sep 1, 2023 | AI Blogs

The following content is about a paper titled “Learning Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning” written by Raffaele Galliera and three other authors. The paper discusses the importance of efficient and...

A Platform for Scientific Challenges: Open Source and Collaborative

by instadatahelp | Sep 1, 2023 | AI Blogs

CodaLab Competitions: An Open Source Platform for Organizing Scientific Challenges Authors: Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu; Published in 24(198):1−6,...
Graph-Based Multi-Agent Reinforcement Learning for Collaborative Information Dissemination

Clustering in the Absence of an Eigengap: A Study (arXiv:2308.15642v1 [cs.LG])

by instadatahelp | Sep 1, 2023 | AI Blogs

In our research, we focus on graph clustering in the Stochastic Block Model (SBM) where there are both large clusters and small, unrecoverable clusters. Previous methods for exact recovery either do not allow small clusters of size less than the square root of the...

Representation Learning Enables Calibrated Multiple-Output Quantile Regression

by instadatahelp | Aug 31, 2023 | AI Blogs

Calibrated Multiple-Output Quantile Regression with Representation Learning Authors: Shai Feldman, Stephen Bates, Yaniv Romano; Journal of Machine Learning Research, 24(24):1−48, 2023. Abstract We present a novel method for generating predictive regions that encompass...
Graph-Based Multi-Agent Reinforcement Learning for Collaborative Information Dissemination

Physics-Informed Neural Networks for Determining Constitutive Parameters in Complex Hyperelastic Solids (arXiv:2308.15640v1 [cond-mat.mtrl-sci])

by instadatahelp | Aug 31, 2023 | AI Blogs

The identification of constitutive parameters in engineering and biological materials, especially those with complex geometries and mechanical behaviors, has long been a challenge. The emergence of Physics-Informed Neural Networks (PINNs) has provided promising...
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