Research
On-device research index

arXiv research

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.

168,742 papers · 148 categories

Trend · papers per month

6.3%12.5%18.8%25.0% · Apr 199319922001200920172026
48 results for rank-one perturbations

This work presents a novel approach to train invertible linear layers by adding rank-one perturbations.

problem Training invertible linear layers during optimization with gradient-based methods is challenging.
method Train rank-one perturbations and add them to weight matrices infrequently, keeping track of inverses and determinants.
result Invertible linear layers improve mixing and mode separation in normalizing flows.

Geodesic spheres in certain symmetric spaces are quantitatively stable under small perturbations.

problem Stability of geodesic spheres in symmetric spaces under perturbations.
method Quantitative stability analysis using spectral gap of the Laplacian on geodesic spheres.
result Geodesic spheres are uniformly stable with respect to small C1C^1-volume preserving perturbations.

Manifolds with fibered hyperbolic cusp metrics include hyperbolic manifolds with cusps and locally symmetric spaces of Q-rank one. We extend Vaillant's treatment of Dirac-type operators associated to these metrics by weaking the hypotheses on the boundary families through the use of Fredholm perturbations as in the fam…

2008-01-14abs ↗pdf ↗

New approach to quantum knot invariants using perturbed Gaussian generating functions.

problem Developing universal quantum knot invariants.
method Introducing generating functions of the form PeGPe^G where GG is quadratic and PP is a perturbation, and developing a calculus for such functions.
result The rank one invariant ZD\mathbf{Z}_\mathbb{D} dominates sl2\mathfrak{sl}_2-colored Jones polynomials and relates to knot genus and Whitehead doubling.

Study on signal-plus-noise decomposition in nonlinear spiked random matrices.

problem Nonlinear spiked random matrix models with rank-one signal and noise.
method Signal-plus-noise decomposition and phase transition analysis.
result Identified precise phase transitions in signal components at critical thresholds.

Estimation of low-rank matrices is of significant interest in a range of contemporary applications. In this paper, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is…

2013-10-22abs ↗pdf ↗

The study establishes uncertainty principles on harmonic manifolds of rank one.

problem Developing uncertainty principles for harmonic manifolds of rank one.
method Derivation of various uncertainty principles including Heisenberg, Morgen, Schrödinger, and Hömanders principles.
result Generalization of Hausdorff-Young inequality to harmonic manifolds of rank one.

Improved stability for matrix recovery from rank-one measurements.

problem Phase retrieval problem of recovering rank-one positive semidefinite matrices.
method Developed a smoothing Newton method based on Bures-Wasserstein gradient descent.
result Superlinear convergence with rigorous guarantees and stable implementation.

Compact rank one symmetric spaces are rigid under certain curvature conditions.

problem Rigidity of compact rank one symmetric spaces under curvature constraints.
method Examined compact symmetric spaces with metric g0g_0 of rank one, and another metric gg with sectional curvature bounded by 0 to 1.
result If gg equals g0g_0 outside a convex subset, then gg is isometric with g0g_0.

We investigate the effect of the dimensionality of the representations learned in Deep Neural Networks (DNNs) on their robustness to input perturbations, both adversarial and random. To achieve low dimensionality of learned representations, we propose an easy-to-use, end-to-end trainable, low-rank regularizer (LR) that…

2018-04-19abs ↗pdf ↗

Novel algorithm for Markov decision processes using rank-one approximation.

problem Solving planning and learning problems of Markov decision processes.
method Policy iteration with rank-one approximation of transition probability matrix.
result The proposed algorithm consistently outperforms first-order algorithms and their accelerated versions.

In this paper, based on research on rank-one isometries by W.Ballmann and M.Brin and recent research on rank-one isometries of Coxeter groups by P.Caprace and K.Fujiwara, we study a topological fractal structure of boundaries of Coxeter groups. We also show that the limit-point set is dense in a boundary of a Coxeter g…

2009-12-01abs ↗pdf ↗

Study shows generative priors improve rank-one matrix recovery with optimal sample complexity.

problem Recovering a rank-one signal matrix from noisy data with additional prior information.
method Analysis of a nonlinear least squares objective with a favorable global optimization landscape.
result Established optimal sample complexity for generative priors in rank-one matrix recovery.

The paper analyzes tensor recovery from symmetric rank-one measurements using information theory.

problem Recovering tensors with low symmetric rank from symmetric rank-one measurements.
method Covering numbers argument, Carbery-Wright inequality, orthogonal polynomials, Fano's inequality.
result Near-optimal sample complexity bounds for log-concave distributions.

Gradient descent solves rank-one matrix estimation problem with detailed time evolution analysis.

problem Estimating a rank-one symmetric matrix corrupted by noise.
method Gradient descent on a sphere, using local versions of the semi-circle law.
result Explicit formulas for the time evolution of the estimator and cost function, revealing phase transitions.

We give examples of rank one compact surfaces on which there exist recurrent geodesics that cannot be shadowed by periodic geodesics. We build rank one compact surfaces such that ergodic measures on the unit tangent bundle of the surface are not dense in the set of probability measures invariant by the geodesic flow. F…

2010-04-29abs ↗pdf ↗