Search Results for author:
Found 6 papers, 1 papers with code
A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images).
Matching Convolutional Neural Networks without Priors about Data
We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs.
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.
Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
We propose a simple and generic layer formulation that extends the properties of convolutional layers to any domain that can be described by a graph.
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency.
Generalizing the Convolution Operator to extend CNNs to Irregular Domains
Convolutional Neural Networks (CNNs) have become the state-of-the-art in supervised learning vision tasks.
