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02/10/2023
DNArch: Learning Convolutional Neural Architectures by Backpropagation
We present Differentiable Neural Architectures (DNArch), a method that j...
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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...
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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...
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04/14/2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Equivariances provide useful inductive biases in neural network modeling...
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10/25/2021
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
Group convolutional neural networks (G-CNNs) have been shown to increase...
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10/19/2021
Learning Equivariances and Partial Equivariances from Data
Group equivariant Convolutional Neural Networks (G-CNNs) constrain featu...
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10/15/2021
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
When designing Convolutional Neural Networks (CNNs), one must select the...
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02/04/2021
CKConv: Continuous Kernel Convolution For Sequential Data
Conventional neural architectures for sequential data present important ...
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10/02/2020
Group Equivariant Stand-Alone Self-Attention For Vision
We provide a general self-attention formulation to impose group equivari...
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06/09/2020
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms
Inducing symmetry equivariance in deep neural architectures has resolved...
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02/07/2020
Attentive Group Equivariant Convolutional Networks
Although group convolutional networks are able to learn powerful represe...
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11/18/2019
