Search Results for author:
Found 14 papers, 10 papers with code
Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries
Group equivariance ensures consistent responses to group transformations of the input, leading to more robust models and enhanced generalization capabilities.
Learned Gridification for Efficient Point Cloud Processing
As a result, operations that rely on neighborhood information scale much worse for point clouds than for grid data, specially for large inputs and large neighborhoods.
DNArch: Learning Convolutional Neural Architectures by Backpropagation
We present Differentiable Neural Architectures (DNArch), a method that jointly learns the weights and the architecture of Convolutional Neural Networks (CNNs) by backpropagation.
Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data.
Towards a General Purpose CNN for Long Range Dependencies in $N$D
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 learning framework.
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example.
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the $\mathrm{Sim(2)}$ group; the group of dilations, rotations and translations.
Ranked #1 on
Rotated MNIST
on Rotated MNIST
Learning Partial Equivariances from Data
Frequently, transformations occurring in data can be better represented by a subset of a group than by a group as a whole, e. g., rotations in $[-90^{\circ}, 90^{\circ}]$.
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
In this work, we propose FlexConv, a novel convolutional operation with which high bandwidth convolutional kernels of learnable kernel size can be learned at a fixed parameter cost.
CKConv: Continuous Kernel Convolution For Sequential Data
Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori.
Ranked #5 on
Sequential Image Classification
on Sequential MNIST
Group Equivariant Stand-Alone Self-Attention For Vision
We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups.
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
In this work, we fill this gap by leveraging the symmetries inherent to time-series for the construction of equivariant neural network.
Attentive Group Equivariant Convolutional Networks
Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (e. g., relative positions and poses).
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data
Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping.
