124 citations · 213 across the 12 of their papers we have counts for
12 papers
Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Framework
K M Anirudh, S Sandeep, Hariprasad Kodamana
Deep atmospheric convection governs South Asian monsoon variability, yet attempting to learn its latent world model directly from zero-inflated, heavy-tailed precipitation yields s…
Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Mridul Gupta, Samyak Jain, Vansh Ramani +2
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of real-world graphs in domains…
Bonsai: Gradient-free Graph Condensation for Node Classification
Mridul Gupta, Samyak Jain, Vansh Ramani +2
Graph condensation has emerged as a promising avenue to enable scalable training of GNNs by compressing the training dataset while preserving essential graph characteristics. Our s…
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
Jyoti Rani, Tapas Tripura, Hariprasad Kodamana +1
Fault detection and isolation in complex systems are critical to ensure reliable and efficient operation. However, traditional fault detection methods often struggle with issues su…
Mirage: Model-Agnostic Graph Distillation for Graph Classification
Mridul Gupta, Sahil Manchanda, Hariprasad Kodamana +1
GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource e…
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
Mridul Gupta, Hariprasad Kodamana, Sayan Ranu
Modelling spatio-temporal processes on road networks is a task of growing importance. While significant progress has been made on developing spatio-temporal graph neural networks (…
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control
Tanuja Joshi, Hariprasad Kodamana, Harikumar Kandath +1
Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting pl…
Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and management
Dibyendu Ghosh, Srija Chakraborty, Hariprasad Kodamana +1
Inclusion of high throughput technologies in the field of biology has generated massive amounts of biological data in the recent years. Now, transforming these huge volumes of data…
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana +1
Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that…
Scalable Gaussian Processes for Predicting the Properties of Inorganic Glasses with Large Datasets
Suresh Bishnoi, R. Ravinder, Hargun Singh +2
Gaussian process regression (GPR) is a useful technique to predict composition--property relationships in glasses as the method inherently provides the standard deviation of the pr…
Deep Learning Aided Rational Design of Oxide Glasses
R. Ravinder, Karthikeya H. Sreedhara, Suresh Bishnoi +5
Despite the extensive usage of oxide glasses for a few millennia, the composition-property relationships in these materials still remain poorly understood. While empirical and phys…
Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning
Suresh Bishnoi, Sourabh Singh, R. Ravinder +4
Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be…