88 citations · 126 across the 18 of their papers we have counts for
9 papers · 1 filter
Are Data Embeddings effective in time series forecasting?
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Time series forecasting plays a crucial role in many real-world applications, and numerous complex forecasting models have been proposed in recent years. Despite their architectura…
Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time…
SPDNet: Seasonal-Periodic Decomposition Network for Advanced Residential Demand Forecasting
Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan
Residential electricity demand forecasting is critical for efficient energy management and grid stability. Accurate predictions enable utility companies to optimize planning and op…
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces
Dylan Wheeler, Balasubramaniam Natarajan
Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic com…
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…
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…
Addressing practical challenges in Active Learning via a hybrid query strategy
Deepesh Agarwal, Pravesh Srivastava, Sergio Martin-del-Campo +2
Active Learning (AL) is a powerful tool to address modern machine learning problems with significantly fewer labeled training instances. However, implementation of traditional AL m…