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06/15/2023
Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data
Given tensors 𝒜, ℬ, 𝒞 of size m × 1 × n, m × p × 1, and 1× p × n, respec...
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03/02/2023
Large Deviations for Accelerating Neural Networks Training
Artificial neural networks (ANNs) require tremendous amount of data to t...
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11/25/2022
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
Complete computation of turbulent combustion flow involves two separate ...
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10/25/2022
Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate
In complex physical process characterization, such as the measurement of...
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09/28/2021
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
Most current anomaly detection methods suffer from the curse of dimensio...
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08/25/2021
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
This study presents an imaging-based deep learning tool to measure the f...
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02/14/2021
A Forward Backward Greedy approach for Sparse Multiscale Learning
Multiscale Models are known to be successful in uncovering and analyzing...
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07/09/2020
ALPS: A Unified Framework for Modeling Time Series of Land Ice Changes
Modeling time series is a research focus in cryospheric sciences because...
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06/09/2020
Hierarchical regularization networks for sparsification based learning on noisy datasets
We propose a hierarchical learning strategy aimed at generating sparse r...
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06/27/2019
Hierarchical Data Reduction and Learning
Paper proposes a hierarchical learning strategy for generation of sparse...
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05/29/2019
Bayesian Anomaly Detection Using Extreme Value Theory
Data-driven anomaly detection methods typically build a model for the no...
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05/30/2017