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Found 11 papers, 2 papers with code
On the rates of convergence for learning with convolutional neural networks
Our second result gives new analysis on the covering number of feed-forward neural networks with CNNs as special cases.
Permutation Equivariant Graph Framelets for Heterophilous Graph Learning
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network models and suggests aggregations beyond the 1-hop neighborhood.
Optimal Estimates for Pairwise Learning with Deep ReLU Networks
Pairwise learning refers to learning tasks where a loss takes a pair of samples into consideration.
SignReLU neural network and its approximation ability
Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years.
Approximation analysis of CNNs from a feature extraction view
In this paper we establish some analysis for linear feature extraction by a deep multi-channel convolutional neural networks (CNNs), which demonstrates the power of deep learning over traditional linear transformations, like Fourier, wavelets, redundant dictionary coding methods.
Learning of Dynamical Systems under Adversarial Attacks -- Null Space Property Perspective
We study the identification of a linear time-invariant dynamical system affected by large-and-sparse disturbances modeling adversarial attacks or faults.
Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks
Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging.
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets
In this paper, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition.
Radial Basis Function Approximation with Distributively Stored Data on Spheres
This paper proposes a distributed weighted regularized least squares algorithm (DWRLS) based on spherical radial basis functions and spherical quadrature rules to tackle spherical data that are stored across numerous local servers and cannot be shared with each other.
Identifying Best Fair Intervention
We study the problem of best arm identification with a fairness constraint in a given causal model.
Theory of Deep Convolutional Neural Networks II: Spherical Analysis
Deep learning based on deep neural networks of various structures and architectures has been powerful in many practical applications, but it lacks enough theoretical verifications.
