
An Introduction to Deep Generative Modeling
Deep generative models (DGM) are neural networks with many hidden layers...
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Mimetic Neural Networks: A unified framework for Protein Design and Folding
Recent advancements in machine learning techniques for protein folding m...
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Secant Penalized BFGS: A Noise Robust QuasiNewton Method Via Penalizing The Secant Condition
In this paper, we introduce a new variant of the BFGS method designed to...
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Segmentation of Pulmonary Opacification in Chest CT Scans of COVID19 Patients
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV2) has rap...
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Fully reversible neural networks for largescale surface and subsurface characterization via remote sensing
The large spatial/frequency scale of hyperspectral and airborne magnetic...
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LeanConvNets: Lowcost Yet Effective Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
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Fluid Flow Mass Transport for Generative Networks
Generative Adversarial Networks have been shown to be powerful in genera...
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Fully Hyperbolic Convolutional Neural Networks
Convolutional Neural Networks (CNN) have recently seen tremendous succes...
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LeanResNet: A Lowcost yet Effective Convolutional Residual Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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Neuralnetworks for geophysicists and their application to seismic data interpretation
Neuralnetworks have seen a surge of interest for the interpretation of ...
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IMEXnet: A Forward Stable Deep Neural Network
Deep convolutional neural networks have revolutionized many machine lear...
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AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Recurrent neural networks have gained widespread use in modeling sequent...
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LargeScale Classification using Multinomial Regression and ADMM
We present a novel method for learning the weights in multinomial logist...
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Automatic classification of geologic units in seismic images using partially interpreted examples
Geologic interpretation of large seismic stacked or migrated seismic ima...
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Multiresolution neural networks for tracking seismic horizons from few training images
Detecting a specific horizon in seismic images is a valuable tool for ge...
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GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
The glymphatic system (GS) is a transit passage that facilitates brain m...
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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 derivativefree optimization method and i...
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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 derivativefree optimization method and i...
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LowCost Parameterizations of Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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LowCost Parameterizations of Deep Convolution Neural Networks
The main computational cost in the training of and prediction with Convo...
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Simultaneous shot inversion for nonuniform geometries using fast data interpolation
Stochastic optimization is key to efficient inversion in PDEconstrained...
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Deep Neural Networks motivated by Partial Differential Equations
Partial differential equations (PDEs) are indispensable for modeling man...
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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...
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Multilevel Residual Networks from Dynamical Systems View
Deep residual networks (ResNets) and their variants are widely used in m...
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Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Recently, deep residual networks have been successfully applied in many ...
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An Efficient Algorithm for MatrixValued and VectorValued Optimal Mass Transport
We present an efficient algorithm for recent generalizations of optimal ...
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Stable Architectures for Deep Neural Networks
Deep neural networks have become invaluable tools for supervised machine...
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Learning across scales  A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and train...
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jInv  a flexible Julia package for PDE parameter estimation
Estimating parameters of Partial Differential Equations (PDEs) from nois...
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Eldad Haber
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