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Found 18 papers, 15 papers with code
Latent Field Discovery In Interacting Dynamical Systems With Neural Fields
Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume that systems evolve in a vacuum.
On genuine invariance learning without weight-tying
We demonstrate that even when a network learns to correctly classify samples on a group orbit, the underlying decision-making in such a model does not attain genuine invariance.
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.
Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis
Motivated by the recent work on separable group convolutions, we devise a SE(3) group convolution kernel separated into a continuous SO(3) (rotation) kernel and a spatial kernel.
An Exploration of Conditioning Methods in Graph Neural Networks
The flexibility and effectiveness of message passing based graph neural networks (GNNs) induced considerable advances in deep learning on graph-structured data.
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.
ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups
Surfing on the success of graph- and group-based neural networks, we take advantage of the recent developments in the geometric deep learning field to derive a new approach to exploit any anisotropies in data.
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations
The high temporal resolution of audio and our perceptual sensitivity to small irregularities in waveforms make synthesizing at high sampling rates a complex and computationally intensive task, prohibiting real-time, controllable synthesis within many approaches.
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
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
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.
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Ranked #5 on
Breast Tumour Classification
on PCam
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).
B-Spline CNNs on Lie Groups
The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides in which rotation equivariance plays a key role and facial landmark localization in which scale equivariance is important.
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
We propose a framework for rotation and translation covariant deep learning using $SE(2)$ group convolutions.
Template Matching via Densities on the Roto-Translation Group
We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns.
