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07/29/2023
ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable success in learning r...
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07/06/2023
LFA-tuned matrix-free multigrid method for the elastic Helmholtz equation
We present an efficient matrix-free geometric multigrid method for the e...
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06/30/2023
Multigrid-Augmented Deep Learning for the Helmholtz Equation: Better Scalability with Compact Implicit Layers
We present a deep learning-based iterative approach to solve the discret...
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03/30/2023
DRIP: Deep Regularizers for Inverse Problems
Inverse problems are mathematically ill-posed. Thus, given some (noisy) ...
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03/06/2023
Graph Positional Encoding via Random Feature Propagation
Two main families of node feature augmentation schemes have been explore...
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02/13/2023
Efficient Graph Laplacian Estimation by a Proximal Newton Approach
The Laplacian-constrained Gaussian Markov Random Field (LGMRF) is a comm...
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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...
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10/31/2022
ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators
Graph Neural Networks (GNNs) are limited in their propagation operators....
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10/21/2022
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Unsupervised image segmentation is an important task in many real-world ...
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07/15/2022
pathGCN: Learning General Graph Spatial Operators from Paths
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural N...
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06/21/2022
Rethinking Unsupervised Neural Superpixel Segmentation
Recently, the concept of unsupervised learning for superpixel segmentati...
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06/07/2022
Inferring Unfairness and Error from Population Statistics in Binary and Multiclass Classification
We propose methods for making inferences on the fairness and accuracy of...
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05/24/2022
Wavelet Feature Maps Compression for Image-to-Image CNNs
Convolutional Neural Networks (CNNs) are known for requiring extensive c...
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05/20/2022
pISTA: preconditioned Iterative Soft Thresholding Algorithm for Graphical Lasso
We propose a novel quasi-Newton method for solving the sparse inverse co...
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03/14/2022
Multigrid-augmented deep learning preconditioners for the Helmholtz equation
In this paper, we present a data-driven approach to iteratively solve th...
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10/10/2021
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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08/31/2021
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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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...
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02/18/2021
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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02/11/2021
Full waveform inversion using extended and simultaneous sources
PDE-constrained optimization problems are often treated using the reduce...
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02/07/2021
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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11/17/2020
Multigrid-in-Channels Neural Network Architectures
We present a multigrid-in-channels (MGIC) approach that tackles the quad...
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06/11/2020
Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks
We present a multigrid approach that combats the quadratic growth of the...
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06/07/2020
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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05/18/2020
Effective Learning of a GMRF Mixture Model
Learning a Gaussian Mixture Model (GMM) is hard when the number of param...
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10/29/2019
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become indispensable for solvi...
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04/23/2019
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
We consider the problem of 3D shape reconstruction from multi-modal data...
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04/15/2019
LeanResNet: A Low-cost yet Effective Convolutional Residual Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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06/29/2018
Shifted Laplacian multigrid for the elastic Helmholtz equation
The shifted Laplacian multigrid method is a well known approach for prec...
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05/20/2018
Low-Cost Parameterizations of Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) filter the input data using a serie...
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05/20/2018
Low-Cost Parameterizations of Deep Convolution Neural Networks
The main computational cost in the training of and prediction with Convo...
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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...
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07/01/2016
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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06/23/2016
