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Found 17 papers, 12 papers with code
Clifford-Steerable Convolutional Neural Networks
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
Hyperbolic Convolutional Neural Networks
However, no papers have yet suggested a general approach to using Hyperbolic Convolutional Neural Networks for structured data processing, although these are the most common examples of data used.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
A Program to Build E(N)-Equivariant Steerable CNNs
This enables us to directly parameterize filters in terms of a band-limited basis on the base space, but also to easily implement steerable CNNs equivariant to a large number of groups.
Steerable Partial Differential Operators for Equivariant Neural Networks
In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential operators (PDOs) in physics.
Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
We argue that the particular choice of coordinatization should not affect a network's inference -- it should be coordinate independent.
A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels
Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance.
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs).
General E(2)-Equivariant Steerable CNNs
Here we give a general description of E(2)-equivariant convolutions in the framework of Steerable CNNs.
Ranked #2 on
Rotated MNIST
on Rotated MNIST
General $E(2)$-Equivariant Steerable CNNs
Here we give a general description of $E(2)$-equivariant convolutions in the framework of Steerable CNNs.
Ranked #34 on
Image Classification
on STL-10
Covariance in Physics and Convolutional Neural Networks
In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs).
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design.
Ranked #23 on
Semantic Segmentation
on Stanford2D3D Panoramic
A General Theory of Equivariant CNNs on Homogeneous Spaces
Feature maps in these networks represent fields on a homogeneous base space, and layers are equivariant maps between spaces of fields.
Explorations in Homeomorphic Variational Auto-Encoding
Our experiments show that choosing manifold-valued latent variables that match the topology of the latent data manifold, is crucial to preserve the topological structure and learn a well-behaved latent space.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3.
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)
In algebraic terms, the feature spaces in regular G-CNNs transform according to a regular representation of the group G, whereas the feature spaces in Steerable G-CNNs transform according to the more general induced representations of G. In order to make the network equivariant, each layer in a G-CNN is required to intertwine between the induced representations associated with its input and output space.
Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input.
Ranked #2 on
Breast Tumour Classification
on PCam
Breast Tumour Classification
Colorectal Gland Segmentation:
+2
