88 citations · 126 across the 18 of their papers we have counts for
18 papers
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…
GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
Priyanka Gautam, Balasubramaniam Natarajan, Sai Munikoti +2
In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strat…
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…
Active Foundational Models for Fault Diagnosis of Electrical Motors
Sriram Anbalagan, Sai Shashank GP, Deepesh Agarwal +2
Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of fa…
Foundational Models for Fault Diagnosis of Electrical Motors
Sriram Anbalagan, Deepesh Agarwal, Balasubramaniam Natarajan +1
A majority of recent advancements related to the fault diagnosis of electrical motors are based on the assumption that training and testing data are drawn from the same distributio…
Knowledge-Driven Semantic Communication Enabled by the Geometry of Meaning
Dylan Wheeler, Balasubramaniam Natarajan
As our world grows increasingly connected and new technologies arise, global demands for data traffic continue to rise exponentially. Limited by the fundamental results of informat…
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…
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan +1
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time…
Semantic Communication with Conceptual Spaces
Dylan Wheeler, Erin E. Tripp, Balasubramaniam Natarajan
Despite the fact that Shannon and Weaver's Mathematical Theory of Communication was published over 70 years ago, all communication systems continue to operate at the first of three…
Engineering Semantic Communication: A Survey
Dylan Wheeler, Balasubramaniam Natarajan
As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Push…
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…
An Information Theoretic approach to identify Dominant Voltage Influencers for Unbalanced Distribution Systems
Sai Munikoti, Mohammad Abujubbeh, Kumarsinh Jhala +1
Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Tradit…
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks
Sai Munikoti, Laya Das, Balasubramaniam Natarajan
In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Gra…