research
∙
07/29/2023
ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable success in learning r...
share
research
∙
03/30/2023
DRIP: Deep Regularizers for Inverse Problems
Inverse problems are mathematically ill-posed. Thus, given some (noisy) ...
research
∙
03/20/2023
Semi-Automated Segmentation of Geoscientific Data Using Superpixels
Geological processes determine the distribution of resources such as cri...
research
∙
11/29/2022
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification
Graph Neural Networks (GNNs) are prominent in handling sparse and unstru...
research
∙
11/25/2022
Neural DAEs: Constrained neural networks
In this article we investigate the effect of explicitly adding auxiliary...
research
∙
08/19/2022
Estimating a potential without the agony of the partition function
Estimating a Gibbs density function given a sample is an important probl...
research
∙
07/15/2022
pathGCN: Learning General Graph Spatial Operators from Paths
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural N...
research
∙
10/18/2021
A-Optimal Active Learning
In this work we discuss the problem of active learning. We present an ap...
research
∙
08/04/2021
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Graph neural networks are increasingly becoming the go-to approach in va...
research
∙
03/09/2021
An Introduction to Deep Generative Modeling
Deep generative models (DGM) are neural networks with many hidden layers...
research
∙
02/07/2021
Mimetic Neural Networks: A unified framework for Protein Design and Folding
Recent advancements in machine learning techniques for protein folding m...
research
∙
10/03/2020
Secant Penalized BFGS: A Noise Robust Quasi-Newton Method Via Penalizing The Secant Condition
In this paper, we introduce a new variant of the BFGS method designed to...
research
∙
07/07/2020
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rap...
research
∙
03/16/2020
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
The large spatial/frequency scale of hyperspectral and airborne magnetic...
research
∙
10/29/2019
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
research
∙
10/03/2019
Fluid Flow Mass Transport for Generative Networks
Generative Adversarial Networks have been shown to be powerful in genera...
research
∙
05/24/2019
Fully Hyperbolic Convolutional Neural Networks
Convolutional Neural Networks (CNN) have recently seen tremendous succes...
research
∙
04/15/2019
LeanResNet: A Low-cost yet Effective Convolutional Residual Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
research
∙
03/27/2019
Neural-networks for geophysicists and their application to seismic data interpretation
Neural-networks have seen a surge of interest for the interpretation of ...
research
∙
03/06/2019
IMEXnet: A Forward Stable Deep Neural Network
Deep convolutional neural networks have revolutionized many machine lear...
research
∙
02/26/2019
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Recurrent neural networks have gained widespread use in modeling sequent...
research
∙
01/27/2019
Large-Scale Classification using Multinomial Regression and ADMM
We present a novel method for learning the weights in multinomial logist...
research
∙
01/12/2019
Automatic classification of geologic units in seismic images using partially interpreted examples
Geologic interpretation of large seismic stacked or migrated seismic ima...
research
∙
12/26/2018
Multi-resolution neural networks for tracking seismic horizons from few training images
Detecting a specific horizon in seismic images is a valuable tool for ge...
research
∙
08/24/2018
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
The glymphatic system (GS) is a transit passage that facilitates brain m...
research
∙
05/21/2018
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation
In this work, we present a new derivative-free optimization method and i...
research
∙
05/21/2018
Never look back - The EnKF method and its application to the training of neural networks without back propagation
In this work, we present a new derivative-free optimization method and i...
research
∙
05/20/2018
Low-Cost Parameterizations of Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
research
∙
05/20/2018
Low-Cost Parameterizations of Deep Convolution Neural Networks
The main computational cost in the training of and prediction with Convo...
research
∙
04/23/2018
Simultaneous shot inversion for nonuniform geometries using fast data interpolation
Stochastic optimization is key to efficient inversion in PDE-constrained...
research
∙
04/12/2018
Deep Neural Networks motivated by Partial Differential Equations
Partial differential equations (PDEs) are indispensable for modeling man...
research
∙
12/17/2017
A multigrid solver to the Helmholtz equation with a point source based on travel time and amplitude
The Helmholtz equation arises when modeling wave propagation in the freq...
research
∙
10/27/2017
Multi-level Residual Networks from Dynamical Systems View
Deep residual networks (ResNets) and their variants are widely used in m...
research
∙
09/12/2017
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Recently, deep residual networks have been successfully applied in many ...
research
∙
06/26/2017
An Efficient Algorithm for Matrix-Valued and Vector-Valued Optimal Mass Transport
We present an efficient algorithm for recent generalizations of optimal ...
research
∙
05/09/2017
Stable Architectures for Deep Neural Networks
Deep neural networks have become invaluable tools for supervised machine...
research
∙
03/06/2017
Learning across scales - A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and train...
research
∙
06/23/2016
