In this paper, we investigate properties and limitations of invariance
l...
Regular group convolutional neural networks (G-CNNs) have been shown to
...
The flexibility and effectiveness of message passing based graph neural
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Performant Convolutional Neural Network (CNN) architectures must be tail...
The use of Convolutional Neural Networks (CNNs) is widespread in Deep
Le...
We introduce ChebLieNet, a group-equivariant method on (anisotropic)
man...
The high temporal resolution of audio and our perceptual sensitivity to ...
Group convolutional neural networks (G-CNNs) have been shown to increase...
When designing Convolutional Neural Networks (CNNs), one must select the...
Conventional neural architectures for sequential data present important
...
Inducing symmetry equivariance in deep neural architectures has resolved...
Rotation-invariance is a desired property of machine-learning models for...
Although group convolutional networks are able to learn powerful
represe...
Group convolutional neural networks (G-CNNs) can be used to improve clas...
We propose a framework for rotation and translation covariant deep learn...
We propose a template matching method for the detection of 2D image obje...