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.
The paper introduces spectral Einstein functionals for Dirac operators on manifolds with boundary.
Paper introduces a new multilinear functional for spectral triples and computes its properties.
We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spe…
The paper examines how gradient descent stabilizes low-rank matrix factorization in noisy conditions.
Study small perturbations on low energy Laplace eigenfunctions.
Study spectral learning for odeco tensors, addressing initialization bottlenecks.
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…
In this paper, we prove a Kastler-Kalau-Walze type theorem for perturbations of Dirac operators on compact manifolds with or without boundary. As a corollary, we give two kinds of operator-theoretic explanations of the gravitational action on boundary. We also compute the spectral action for Dirac operators with two-fo…
Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between per…
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged as prevailing methods for efficiently recovering sparse and partially observed signals respectively. We combine the distance preserving measur…
Spectral methods simplify data analysis, improving accuracy and stability.
We study the relationship between Bar-Natan's perturbation in Khovanov homology and Szabo's geometric spectral sequence, and construct a link invariant that generalizes both into a common theory. We study a few properties of the new invariant, and introduce a family of s-invariants from the new theory in the same spiri…
The paper addresses uncertainties in spectral clustering of corrupted data.
Deterministic bounds for tensor singular values and vectors, differing from matrix cases.
Geodesic spheres in certain symmetric spaces are quantitatively stable under small perturbations.
Computing Chern-Simons action for perturbed Dirac triples
Laplacian Eigenvectors of the graph constructed from a data set are used in many spectral manifold learning algorithms such as diffusion maps and spectral clustering. Given a graph constructed from a random sample of a -dimensional compact submanifold in , we establish the spectral convergence rate…
New method improves matrix completion accuracy, especially in noisy data.
Extends Einstein-Hilbert action to higher-order spectral triples.
A fast, robust AMP algorithm for quadratic optimization problems.
New framework for higher-order singular-value derivatives of rectangular matrices.
Spectral portfolio theory links neural networks to wealth dynamics via SGD weight matrices.
A method to explain disease transformation using biomarker covariance matrices.
The paper calculates spectral torsion for rescaled Dirac operators on manifolds.
We study the (massless) Dirac operator on a 3-sphere equipped with Riemannian metric. For the standard metric the spectrum is known. In particular, the eigenvalues closest to zero are the two double eigenvalues +3/2 and -3/2. Our aim is to analyse the behaviour of eigenvalues when the metric is perturbed in an arbitrar…
Given the apparent difficulty of learning models that are robust to adversarial perturbations, we propose tackling the simpler problem of developing adversarially robust features. Specifically, given a dataset and metric of interest, the goal is to return a function (or multiple functions) that 1) is robust to adversar…
The paper analyzes how quantization affects the Fisher Information Matrix's dominant eigenvalue.
Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to the input, which undermines their true practicality. Recent works have increased the robustness of DNNs by fitting networks using adversarially…
We derive a gauge theoretic invariant of integral homology 3-spheres which counts gauge orbits of irreducible, perturbed flat SU(3) connections with sign given by spectral flow. To compensate for the dependence of this sum on perturbations, the invariant includes contributions from the reducible, perturbed flat orbits.…
We study a regularizer which is defined as a parameterized infimum of quadratics, and which we call the box-norm. We show that the k-support norm, a regularizer proposed by [Argyriou et al, 2012] for sparse vector prediction problems, belongs to this family, and the box-norm can be generated as a perturbation of the fo…
Spectral clustering for geometric graphs achieves strong consistency in community recovery.
Spectral graph sparsification preserves geometry of GNN embeddings.
This work uses Lyapunov theory to improve the robustness of deep neural networks against adversarial attacks.
Researchers calculate exact moduli for type II flux backgrounds using spectral sequences.
This work analyzes how different forms of compressibility affect adversarial robustness in neural networks.
We develop techniques for computing the integer valued SU(3) Casson invariant. Our method involves resolving the singularities in the flat moduli space using a twisting perturbation and analyzing its effect on the topology of the perturbed flat moduli space. These techniques, together with Bott-Morse theory and the spl…
New framework for conformal equivariant cycles in KK-theory.
In a noncommutative torus, effect of perturbation by inner derivation on the associated quantum stochastic process and geometric parameters like volume and scalar curvature have been studied. Cohomological calculations show that the above perturbation produces new spectral triples. Also for the Weyl C^*-algebra, the La…
The paper shows how solutions of perturbed Dirac operators concentrate near singular sets.
Classical matrix perturbation results, such as Weyl's theorem for eigenvalues and the Davis-Kahan theorem for eigenvectors, are general purpose. These classical bounds are tight in the worst case, but in many settings sub-optimal in the typical case. In this paper, we present perturbation bounds which consider the natu…
Simple technique turns any adversarial attack into a universal one using few test examples.
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…
New framework assesses neural sensitivity to small perturbations.
The existence and continuity for the Calderon projector of the perturbed odd signature operator on a 3-manifold is established. As an application we give a new proof of a result of Taubes relating the mod 2 spectral flow of a family of operators on a homology 3-sphere with the difference in local intersection numbers o…
Perturbation theory improves nonparametric instrumental variable estimation accuracy.
We compute the Ricci curvature of a curved noncommutative three torus. The computation is done both for conformal and non-conformal perturbations of the flat metric. To perturb the flat metric, the standard volume form on the noncommutative three torus is perturbed and the corresponding perturbed Laplacian is analyzed.…
New spectral clustering method using LASSO regularization for robust graph partitioning.
Spectral clustering achieves strong consistency in the stochastic block model under certain conditions.