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07/31/2023
Foundational Models for Fault Diagnosis of Electrical Motors
A majority of recent advancements related to the fault diagnosis of elec...
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06/01/2023
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Uncertainty quantification is a critical yet unsolved challenge for deep...
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06/16/2022
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial ...
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05/30/2022
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
Influence maximization (IM) is a combinatorial problem of identifying a ...
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05/20/2022
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Graph Neural Networks (GNN) provide a powerful framework that elegantly ...
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10/07/2021
Addressing practical challenges in Active Learning via a hybrid query strategy
Active Learning (AL) is a powerful tool to address modern machine learni...
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12/26/2020
Bayesian Inductive Learner for Graph Resiliency under uncertainty
In the quest to improve efficiency, interdependence and complexity are b...
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12/26/2020
Graph neural network based approximation of Node Resiliency in complex networks
The emphasis on optimal operations and efficiency has led to increased c...
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03/10/2020
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks
Emerging wireless services with extremely high data rate requirements, s...
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09/09/2019
