62 citations · 178 across the 15 of their papers we have counts for
5 papers · 1 filter
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan
Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this p…
There is more to graphs than meets the eye: Learning universal features with self-supervision
Laya Das, Sai Munikoti, Nrushad Joshi +1
We study the problem of learning features through self-supervision that are generalisable to multiple graphs. State-of-the-art graph self-supervision restricts training to only one…
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Sai Munikoti, Deepesh Agarwal, Laya Das +2
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly,…
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
Sai Munikoti, Balasubramaniam Natarajan, Mahantesh Halappanavar
Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread o…
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Sai Munikoti, Deepesh Agarwal, Laya Das +1
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the p…