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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.

169,051 papers · 148 categories

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48 results for Jacobian locus

The study finds infinitely many Shimura subvarieties in Jacobian loci for curves of genus 2, 3, and 4.

problem Understanding Shimura subvarieties in Jacobian loci for curves of positive genus.
method Analyzing Galois covers of curves and their Shimura subvarieties under specific numerical conditions.
result The Jacobian locus contains infinitely many Shimura subvarieties of positive dimension for g4g \leq 4.

Let Mg\mathcal M_g denote the moduli space of compact Riemann surfaces of genus gg and let Ag\mathcal A_g be the space of principally polarized abelian varieties of (complex) dimension gg. Let J:MgAgJ:\mathcal M_g\longrightarrow \mathcal A_g be the map which associates to a Riemann surface its Jacobian. The map JJ is in…

2008-11-25abs ↗pdf ↗

In this paper, we first single out a proper subgroup Γof Sp(4,Z) generated by three elements, which arises from the parallelogram decompositions of translation surfaces in H(2). We then prove that the space H(2)/C* can be identified to the quotient J_2/Γ, where J_2 is the Jacobian locus in the Siegel upper half space H…

2010-03-03abs ↗pdf ↗

This paper is devoted to the classification of connected components of Prym eigenform loci in the strata H(2,2)^odd and H(1,1,2) in the Abelian differentials bundle in genus 3. These loci, discovered by McMullen are GL^+(2,R)-invariant submanifolds (of complex dimension 3) that project to the locus of Riemann surfaces …

2014-08-05abs ↗pdf ↗

The rational cohomology ring of A_3, the moduli space of abelian 3-folds is computed. This is isomorphic to the the rational cohomology ring of the group Sp_3(Z) of 6x6 integral symplectic matrices. The main ingredients in the computation are (1) Looijenga's computation of the rational cohomology ring of M_3, the modul…

2002-03-06abs ↗pdf ↗

This study connects Jacobian regularization to adversarial robustness and improves generalization.

problem Adversarial attacks make deep neural networks vulnerable.
method Developed a connection between Jacobian regularization and adversarial training, and established robust generalization gaps.
result Jacobian norms are related to both standard and robust generalization.

The period mapping assigns to each rank n, marked metric graph Gamma a positive definite quadratic form on H_1(Gamma). This defines maps Phi* and Phi on Culler--Vogtmann's outer space CV_n, and its Torelli space quotient T_n, respectively. The map Phi is a free group analog of the classical period mapping that sends a …

2016-09-12abs ↗pdf ↗

Deep neural networks' Jacobian spectrum becomes well-conditioned with orthogonal weights.

problem Understanding and handling the Jacobian spectrum of deep neural networks.
method Applying free probability theory to show almost sure asymptotic freeness of Jacobians in the wide limit.
result Layer-wise Jacobians of deep neural networks with orthogonal weights are almost surely asymptotically free.

Fractional Sobolev maps with positive distributional Jacobians are continuous.

problem Proving continuity of maps in fractional Sobolev spaces with positive Jacobians.
method Extending known results from W1,nW^{1,n} to Ws,nsW^{s,\frac{n}{s}} for snn+1s \geq \frac{n}{n+1}, considering distributional Jacobians.
result Fractional Sobolev maps with positive distributional Jacobians are continuous.

This work proves the asymptotic freeness of layerwise Jacobians in MLPs with Haar orthogonal matrices.

problem Proving the asymptotic freeness of layerwise Jacobians in multilayer perceptrons (MLPs).
method Replacing each layer's parameter matrix with itself multiplied by a Haar orthogonal matrix, and using the invariance of the MLP.
result Proves the asymptotic freeness of layerwise Jacobians in MLPs with Haar orthogonal matrices.

Efficiently regularizes deep learning models using Jacobian nuclear norm.

problem Regularizing deep learning models to prevent overfitting and improve generalization.
method Proposes a denoising-style approximation to penalize the Jacobian nuclear norm without computing the Jacobian matrix.
result Demonstrates that penalizing the average squared Frobenius norm of JgJg and JhJh is equivalent to penalizing the Jacobian nuclear norm for function compositions.

Jacobian regularization boosts neural network robustness without degrading generalization.

problem Ensuring robustness of machine learning models against input perturbations.
method Developed a computationally efficient Jacobian regularization technique.
result Significant improvements in robustness measured against random and adversarial perturbations.

Paper tackles Hessian/Jacobian-free stochastic bilevel optimization with O(ε1.5){O}(ε^{-1.5}) complexity.

problem Nonconvex-strongly-convex bilevel optimization problem.
method FdeHBO optimizer with finite-difference Hessian/Jacobian-vector approximation and momentum.
result FdeHBO achieves O(ε1.5){O}(ε^{-1.5}) iterations for εε-accurate stationary point.

Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find th…

2018-02-23abs ↗pdf ↗

The paper discusses fractional Sobolev immersions of flat domains into 3D space.

problem Developing C1C^1 regularity and isometric immersions of flat domains with fractional Sobolev regularity.
method Analysis of weak Codazzi-Mainardi equations, study of $W^{2, rac2s}$ planar deformations, and properties of the distributional Jacobian determinant.
result Generalization of isometric immersions with local fractional Sobolev regularity.

We derive an analytic formula for the dual Jacobian matrix of a generalised hyperbolic tetrahedron. Two cases are considered: a mildly truncated and a prism truncated tetrahedron. The Jacobian for the latter arises as an analytic continuation of the former, that falls in line with a similar behaviour of the correspondi…

2014-09-11abs ↗pdf ↗

The paper extends infinite-width analysis to neural network Jacobians, revealing convergence to Gaussian processes and linear ODEs.

problem Understanding the training dynamics of neural networks in the infinite-width limit.
method Extending infinite-width analysis to Jacobians, characterizing convergence to Gaussian processes and linear ODEs.
result The evolution of MLPs under robust training in the infinite-width limit is described by a linear ODE.

RecurJac efficiently computes Jacobian bounds for neural networks.

problem Computing Jacobian bounds for neural networks efficiently and accurately.
method Recursive algorithm to compute upper and lower bounds for Jacobian matrix elements.
result Our method produces better quality Lipschitz constants than previous approaches.

New algorithms estimate Jacobian matrices for large-scale machine learning.

problem Efficiently computing search directions for large nonlinear least squares.
method Exploit low-rank structure in Hessian to estimate Jacobian matrices.
result Two algorithms perform well compared to state-of-the-art methods.

Study the geometry of lightlike loci on mixed type surfaces in Lorentz-Minkowski 3-space.

problem Characterize the differential geometric properties of lightlike loci on mixed type surfaces.
method Define a frame field and lightlike ruled surfaces along the lightlike locus, analyze their singularities and intersections.
result Establish a relationship between the singularities of lightlike ruled surfaces and the differential geometric properties of the lightlike locus.

GrokAlign aligns Jacobians to accelerate grokking in deep networks.

problem Accelerating the training dynamics of deep networks to avoid delayed generalisation and robustness.
method Aligning the Jacobians of a deep network with the training data to ensure grokking under a low-rank assumption.
result GrokAlign regularizes Jacobians to induce grokking sooner than conventional methods.

New method reduces deep learning training costs by approximating vector-jacobian products.

problem Efficiently training deep neural networks with reduced computational and memory costs.
method Randomized, unbiased approximations of vector-jacobian products during backpropagation.
result Validated potential for reducing deep learning training costs through unbiased estimates.

The aim of this article is to generalize the notion of the cut locus and to get the structure theorem for it. For this purpose, we first introduce a class of 1-Lipschitz functions, each member of which is called an {\it almost distance function}. Typical examples of an almost distance function are the distance function…

2018-03-12abs ↗pdf ↗

Normalizing flows optimize Jacobian determinant for unique likelihood objective.

problem Optimizing normalizing flows for unique likelihood.
method Showed Jacobian determinant is unique for given distributions, leading to a unique global optimum. Used eigenvalues of auto-correlation matrix for explicit likelihood expression.
result Explicit expression of likelihood for flows, independent of neural network parameterization, with theoretical optimal value.

The Jacobian of Douady-Earle extension equals 1 only for isometries.

problem Investigating the Jacobian of Douady-Earle extension maps.
method Analyzing the Jacobian of the Douady-Earle extension map and constructing sequences of hyperbolic surfaces.
result The Jacobian of the Douady-Earle extension map is 1 only when the map is an isometry, and it can grow arbitrarily large for certain sequences of surfaces.

This thesis explores algebraic cycles and moduli spaces over real numbers.

problem Understanding the cycle class map and its image in real algebraic geometry.
method Constructing integral Fourier transforms on Chow rings of abelian varieties over any field.
result Proof of integral Hodge conjecture for real abelian threefolds and moduli space properties.

To a compact Riemann surface of genus g can be assigned a principally polarized abelian variety (PPAV) of dimension g, the Jacobian of the Riemann surface. The Schottky problem is to discern the Jacobians among the PPAVs. Buser and Sarnak showed, that the square of the first successive minimum, the squared norm of the …

2010-08-12abs ↗pdf ↗

We show that the Goldman flows preserve the holomorphic structure on the moduli space of homomorphisms of the fundamental group of a Riemann surface into U(1), in other words the Jacobian.

2008-02-24abs ↗pdf ↗

In this study, we investigate the locus of the centers of the Meusnier spheres. Just as focal curve is the locus of the centers of the osculating spheres, we investigate the geometrical interpretation on the locus of the centers of the Meusnier spheres. We proved that if the curve is a principal line, the locus of the …

2013-07-16abs ↗pdf ↗

We study the singular locus of solutions to Hamilton-Jacobi equations with a Hamiltonian independent of uu. In a previous paper, we proved that the singular locus is what we call a balanced split locus. In this paper, we find and classify all balanced split sets, identifying the cases where the only balanced split loc…

2008-07-13abs ↗pdf ↗

The paper describes the CR umbilical locus of a real ellipsoid in complex space.

problem Characterizing the CR umbilical locus of a real ellipsoid in complex space.
method Analyzing the set of points where the ellipsoid can be osculated by a biholomorphic image of the sphere up to 6th order.
result The CR umbilical locus is the union of stable curves and a non-trivial real variety defined by sextic equations.

Laplacian of distance function shows negative infinity at cut locus points.

problem Understanding the Laplacian of distance functions on Riemannian manifolds.
method Analyzing the Laplacian of the distance function to a point on a smooth Riemannian manifold.
result The Laplacian of the distance function is -\infty at points of the cut locus.