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Found 10 papers, 4 papers with code
Unitary convolutions for learning on graphs and groups
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-invariance in images.
Hardness of Learning Neural Networks under the Manifold Hypothesis
In this paper, we investigate the hardness of learning under the manifold hypothesis.
On the hardness of learning under symmetries
We study the problem of learning equivariant neural networks via gradient descent.
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering.
Equivariant Polynomials for Graph Neural Networks
This paper introduces an alternative expressive power hierarchy based on the ability of GNNs to calculate equivariant polynomials of a certain degree.
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision.
Joint Embedding Self-Supervised Learning in the Kernel Regime
The fundamental goal of self-supervised learning (SSL) is to produce useful representations of data without access to any labels for classifying the data.
Implicit Bias of Linear Equivariant Networks
Group equivariant convolutional neural networks (G-CNNs) are generalizations of convolutional neural networks (CNNs) which excel in a wide range of technical applications by explicitly encoding symmetries, such as rotations and permutations, in their architectures.
Quantum algorithms for group convolution, cross-correlation, and equivariant transformations
Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used in mathematics to analyze or take advantage of symmetries inherent in a given problem setting.
Adversarial Robustness Guarantees for Random Deep Neural Networks
We explore the properties of adversarial examples for deep neural networks with random weights and biases, and prove that for any $p\ge1$, the $\ell^p$ distance of any given input from the classification boundary scales as one over the square root of the dimension of the input times the $\ell^p$ norm of the input.
