
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are widely used in a variety of appl...
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Quantized convolutional neural networks through the lens of partial differential equations
Quantization of Convolutional Neural Networks (CNNs) is a common approac...
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PDEGCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Graph neural networks are increasingly becoming the goto approach in va...
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GradFreeBits: Gradient Free Bit Allocation for Dynamic Low Precision Neural Networks
Quantized neural networks (QNNs) are among the main approaches for deplo...
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Full waveform inversion using extended and simultaneous sources
PDEconstrained optimization problems are often treated using the reduce...
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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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MultigridinChannels Neural Network Architectures
We present a multigridinchannels (MGIC) approach that tackles the quad...
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MultigridinChannels Architectures for Wide Convolutional Neural Networks
We present a multigrid approach that combats the quadratic growth of the...
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DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Graph Convolutional Networks (GCNs) have shown to be effective in handli...
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Effective Learning of a GMRF Mixture Model
Learning a Gaussian Mixture Model (GMM) is hard when the number of param...
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LeanConvNets: Lowcost Yet Effective Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
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Multimodal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
We consider the problem of 3D shape reconstruction from multimodal data...
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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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Shifted Laplacian multigrid for the elastic Helmholtz equation
The shifted Laplacian multigrid method is a well known approach for prec...
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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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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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A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression
Solving l1 regularized optimization problems is common in the fields of ...
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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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Eran Treister
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