31 citations · 125 across the 15 of their papers we have counts for
7 papers · 1 filter
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
David R. Wessels, Farhad Ramezanghorbani, Alireza Moradzadeh +9
Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while re…
Fast, Expressive SE Equivariant Networks through Weight-Sharing in Position-Orientation Space
Erik J Bekkers, Sharvaree Vadgama, Rob D Hesselink +2
Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework. We formalize the notion of weigh…
DNArch: Learning Convolutional Neural Architectures by Backpropagation
David W. Romero, Neil Zeghidour
We present Differentiable Neural Architectures (DNArch), a method that jointly learns the weights and the architecture of Convolutional Neural Networks (CNNs) by backpropagation. I…
Towards a General Purpose CNN for Long Range Dependencies in D
David W. Romero, David M. Knigge, Albert Gu +4
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learn…
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example. Equivariance…
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers +2
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizo…
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and…