A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a Gaussian process for graph signals using adaptive spectral kernels.
Study on discrepancy principle for learning algorithms in nonparametric regression.
A new convolutional spectral kernel network learns hierarchical and local features.
The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to kernel methods in that it is used by classical approaches (e.g., when centering a kernel PCA matrix), and it also forms the core inference step of modern kernel methods (e.g., kernel-based non-parametric tests) that rel…
New wavelet frames constructed from reproducing kernels for continuous and discrete domains.
Graph classification improved using spectral features and wavelet filters.
Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a stat…
Deep learning explained through spectral filtering of hierarchical features.
This paper tackles the curse of dimensionality in semi-supervised learning using Laplacian regularization.
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…
We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited. As a complement to the growing literature on spectral design of covariance kernels, the core of our proposal is to model power spectral densities through a rect…
New method simplifies tomographic reconstruction using RKHS.
Paper tackles functional linear regression using spectral algorithms with discrete observations.
New method filters large networks from financial data to reveal key subnetworks.
Developed a framework for designing filters in spectral GCNNs with improved performance.
New defence against data-poisoning attacks in neural networks.
Kernel learning FBSDE filter improves nonlinear filtering efficiency.
Paper revisits graph-CNNs using Laplace-Beltrami spectral filters and polynomials.
EnEMF uses Epanechnikov kernel for high-dimensional filtering, improving accuracy and robustness.
Algorithm learns dynamics from past observations.
New equivariant filters improve graph classification.
Spectral clustering is one of the most popular methods for community detection in graphs. A key step in spectral clustering algorithms is the eigen decomposition of the graph Laplacian matrix to extract its leading eigenvectors, where is the desired number of clusters among objects. This is pro…
Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In the recent years, significant attention has been devoted to descriptors obtained from the spectral decomposi…
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a…
This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are deter…
New spectral mixture representation for isotropic kernels simplifies random Fourier features.
In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i.e. can approximate any bounded kernel with arbitrary precision). We start by discussing the case of stationary kernels, and propose a family of spectral kernels that extends existing approac…
Regularization techniques are widely used to improve the generality, robustness, and efficiency of deep convolutional neural networks (DCNNs). In this paper, we propose a novel approach of regulating DCNN convolutional kernels by a structured filter bank. Comparing with the existing regularization methods, such as $\el…
New method for spectral and Bergman kernels under local spectral gap condition.
The present paper proposes generalized Gaussian kernel adaptive filtering, where the kernel parameters are adaptive and data-driven. The Gaussian kernel is parametrized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of the scalar width parameter. These…
New complexes derived from any filtered cochain complex compute the same cohomology.
Spectro-Riemannian Graph Neural Networks integrate spectral and curvature signals for better graph representation learning.
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals defined on many different graphs, the trained ConvNet should generalize to signals…
We propose a novel spectral convolutional neural network (CNN) model on graph structured data, namely Distributed Feedback-Looped Networks (DFNets). This model is incorporated with a robust class of spectral graph filters, called feedback-looped filters, to provide better localization on vertices, while still attaining…
DNNs improve SIMP method but not spatially invariant, study shows.
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default kernel in KAF algorithms, but selecting the proper kernel size (bandwidth) is still an open important issue especially for learning with small sample s…
Paper presents a fast and adaptive filter for SI suppression in full-duplex transceivers.
Standard kernels such as Matérn or RBF kernels only encode simple monotonic dependencies within the input space. Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data. Spectral mixture kernels have recently been generalized in…
Ridge regression linked to Poisson resetting in statistical physics.
This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowl…
Transformers can approximate Kalman Filtering in linear systems with small error.
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks. In this paper, we propose a powerful and efficient spectral kernel learning framework and learned ke…
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more …
New inequalities for spectral zeta kernels on spheres and manifolds.
Generative model controls heterophily in graph signals.
SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.
Unified framework for spectral methods, kernel learning, and manifold unfolding.