
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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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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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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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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Moshe Eliasof
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