Erik J. Bekkers | DeepAI

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∙ 05/03/2023

An Exploration of Conditioning Methods in Graph Neural Networks

The flexibility and effectiveness of message passing based graph neural ...

0 Yeskendir Koishekenov, et al. ∙

∙ 01/25/2023

Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN

Performant Convolutional Neural Network (CNN) architectures must be tail...

0 David M. Knigge, et al. ∙

∙ 06/07/2022

Towards a General Purpose CNN for Long Range Dependencies in ND

The use of Convolutional Neural Networks (CNNs) is widespread in Deep Le...

26 David W. Romero, et al. ∙

∙ 11/23/2021

ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups

We introduce ChebLieNet, a group-equivariant method on (anisotropic) man...

3 Hugo Aguettaz, et al. ∙

∙ 11/14/2021

Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations

The high temporal resolution of audio and our perceptual sensitivity to ...

0 Jan Zuiderveld, et al. ∙

∙ 10/25/2021

Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups

Group convolutional neural networks (G-CNNs) have been shown to increase...

0 David M. Knigge, et al. ∙

∙ 10/15/2021

FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes

When designing Convolutional Neural Networks (CNNs), one must select the...

0 David W. Romero, et al. ∙

∙ 02/04/2021

CKConv: Continuous Kernel Convolution For Sequential Data

Conventional neural architectures for sequential data present important ...

0 David W. Romero, et al. ∙

∙ 06/09/2020

Wavelet Networks: Scale Equivariant Learning From Raw Waveforms

Inducing symmetry equivariance in deep neural architectures has resolved...

0 David W. Romero, et al. ∙

∙ 02/20/2020

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

Rotation-invariance is a desired property of machine-learning models for...

61 Maxime W. Lafarge, et al. ∙

∙ 02/07/2020

Attentive Group Equivariant Convolutional Networks

Although group convolutional networks are able to learn powerful represe...

14 David W. Romero, et al. ∙

∙ 09/26/2019

B-Spline CNNs on Lie Groups

Group convolutional neural networks (G-CNNs) can be used to improve clas...

0 Erik J. Bekkers, et al. ∙

∙ 04/10/2018

Roto-Translation Covariant Convolutional Networks for Medical Image Analysis

We propose a framework for rotation and translation covariant deep learn...

0 Erik J. Bekkers, et al. ∙

∙ 03/10/2016

Template Matching via Densities on the Roto-Translation Group

We propose a template matching method for the detection of 2D image obje...

0 Erik J. Bekkers, et al. ∙