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Found 18 papers, 2 papers with code
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
Load forecasting is a fundamental task in smart grid.
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
Graph convolutional networks (GCNs) have emerged as powerful models for graph learning tasks, exhibiting promising performance in various domains.
Spherical Analysis of Learning Nonlinear Functionals
It utilizes spherical harmonics to help us extract the latent finite-dimensional information of functions, which in turn facilitates in the next step of approximation analysis using fully connected neural networks.
Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks
In this work, we explore intersections between sparse coding and deep learning to enhance our understanding of feature extraction capabilities in advanced neural network architectures.
Theoretical Insights into CycleGAN: Analyzing Approximation and Estimation Errors in Unpaired Data Generation
These two error terms are analyzed separately and ultimately combined by considering the trade-off between them.
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
In our numerical experiments, we analyze several widely applied GCNs and observe the phenomenon of energy decay.
Stratified Avatar Generation from Sparse Observations
This task is challenging due to the limited input from Head Mounted Devices, which capture only sparse observations from the head and hands.
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.
Fine-grained analysis of non-parametric estimation for pairwise learning
In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning.
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.
