In this paper, we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches. We develop an ...

Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and usually only ...

Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. "Convolutional neural networks on graphs with fast localized spectral filtering." Advances in Neural Information Processing Systems. 2016 ...

And since neural graph networks require modified convolution and pooling operators, many Python packages like PyTorch Geometric, StellarGraph, and DGL have emerged for working with graphs. In Keras ...

A Higher-Order Graph Convolutional Layer. Sami A Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Hrayr Harutyunyan. NeurIPS, 2018. [link] In traditional Convolutional Neural Networks ...

Convolution layers (the use of a filter to create a feature map) run from 1D to 3D and include the most common variants, such as cropping and transposed convolution layers for each dimensionality ...

We test and analyze the performance of the convolutional network both unconditionally and conditionally for financial time series forecasting using the Standard & Poor’s 500 index, the volatility ...