Graph neural networks (GNNs) have shown remarkable success in learning
r...
Inverse problems are mathematically ill-posed. Thus, given some (noisy) ...
Geological processes determine the distribution of resources such as cri...
Graph Neural Networks (GNNs) are prominent in handling sparse and
unstru...
In this article we investigate the effect of explicitly adding auxiliary...
Estimating a Gibbs density function given a sample is an important probl...
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural
N...
In this work we discuss the problem of active learning. We present an
ap...
Graph neural networks are increasingly becoming the go-to approach in va...
Deep generative models (DGM) are neural networks with many hidden layers...
Recent advancements in machine learning techniques for protein folding
m...
In this paper, we introduce a new variant of the BFGS method designed to...
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rap...
The large spatial/frequency scale of hyperspectral and airborne magnetic...
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
Generative Adversarial Networks have been shown to be powerful in genera...
Convolutional Neural Networks (CNN) have recently seen tremendous succes...
Convolutional Neural Networks (CNNs) filter the input data using a serie...
Neural-networks have seen a surge of interest for the interpretation of
...
Deep convolutional neural networks have revolutionized many machine lear...
Recurrent neural networks have gained widespread use in modeling sequent...
We present a novel method for learning the weights in multinomial logist...
Geologic interpretation of large seismic stacked or migrated seismic ima...
Detecting a specific horizon in seismic images is a valuable tool for
ge...
The glymphatic system (GS) is a transit passage that facilitates brain
m...
In this work, we present a new derivative-free optimization method and
i...
In this work, we present a new derivative-free optimization method and
i...
Convolutional Neural Networks (CNNs) filter the input data using a serie...
The main computational cost in the training of and prediction with
Convo...
Stochastic optimization is key to efficient inversion in PDE-constrained...
Partial differential equations (PDEs) are indispensable for modeling man...
The Helmholtz equation arises when modeling wave propagation in the freq...
Deep residual networks (ResNets) and their variants are widely used in m...
Recently, deep residual networks have been successfully applied in many
...
We present an efficient algorithm for recent generalizations of optimal ...
Deep neural networks have become invaluable tools for supervised machine...
In this work we establish the relation between optimal control and train...
Estimating parameters of Partial Differential Equations (PDEs) from nois...