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10/07/2022
A deep learning approach to solve forward differential problems on graphs
We propose a novel deep learning (DL) approach to solve one-dimensional ...
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10/07/2022
A deep learning approach for detection and localization of leaf anomalies
The detection and localization of possible diseases in crops are usually...
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07/25/2022
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks
We propose a stable, parallel approach to train Wasserstein Conditional ...
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07/22/2022
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep lea...
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06/08/2022
Anderson acceleration with approximate calculations: applications to scientific computing
We provide rigorous theoretical bounds for Anderson acceleration (AA) th...
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04/01/2022
Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data
We propose a new approach to generate a reliable reduced model for a par...
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02/04/2022
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
We introduce a multi-tasking graph convolutional neural network, HydraGN...
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10/26/2021
Stable Anderson Acceleration for Deep Learning
Anderson acceleration (AA) is an extrapolation technique designed to spe...
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02/21/2021
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
We propose a distributed approach to train deep convolutional generative...
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09/07/2019