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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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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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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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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...
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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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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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02/07/2021
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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06/07/2020
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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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