15 citations · 61 across the 20 of their papers we have counts for
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Large Deviations for Accelerating Neural Networks Training
Sreelekha Guggilam, Varun Chandola, Abani Patra
Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase…
Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility
Syed Mohammed Arshad Zaidi, Varun Chandola, EunHye Yoo
Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling…
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
Amol Salunkhe, Georgios Georgalis, Abani Patra +1
Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold durin…
Physics Informed Machine Learning for Chemistry Tabulation
Amol Salunkhe, Dwyer Deighan, Paul Desjardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
ChemTab: A Physics Guided Chemistry Modeling Framework
Amol Salunkhe, Dwyer Deighan, Paul DesJardin +1
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Inste…
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
Sreelekha Guggilam, Varun Chandola, Abani Patra
Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to…
Learning Deep Representations from Clinical Data for Chronic Kidney Disease
Duc Thanh Anh Luong, Varun Chandola
We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a state-of-the-art method, in the context of learning latent representations from irregularly sampled pati…
Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models
Jialiang Jiang, Sharon Hewner, Varun Chandola
Hospital readmissions have become one of the key measures of healthcare quality. Preventable readmissions have been identified as one of the primary targets for reducing costs and…