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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.

168,657 papers · 148 categories

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142284425567 · Jun 202019922001200920172026
48 results for tangent distributions

We study codimension one holomorphic distributions on the projective three-space, analyzing the properties of their singular schemes and tangent sheaves. In particular, we provide a classification of codimension one distributions of degree at most 2 with locally free tangent sheaves, and show that codimension one distr…

2016-11-17abs ↗pdf ↗

Study minimal rational curves on complex manifolds with isotropic VMRT.

problem Understanding minimal rational curves tangent to distributions on complex manifolds.
method Partial equivariant compactification of metabelian groups.
result Any isotropic VMRT can be realized as VMRT of minimal rational curves tangent to a distribution.

We continue the program of structural differential geometry that begins with the notion of a tangent category, an axiomatization of structural aspects of the tangent functor on the category of smooth manifolds. In classical geometry, having an affine structure on a manifold is equivalent to having a flat torsion-free c…

2018-07-25abs ↗pdf ↗

Geodesics of the same type on curved surfaces are randomly distributed.

problem Distribution of geodesics of the same type on negatively curved surfaces.
method Asymptotic equidistribution with respect to a measure on the unit tangent bundle.
result Geodesics of the same type are asymptotically equidistributed with respect to a measure mS\mathfrak{m}^S.

In this paper we study J~\widetilde{J}-tangent affine hyperspheres, where J~\widetilde{J} is the canonical para-complex structure on R2n+2\mathbb{R}^{2n+2}. The main purpose of this paper is to give a classification of J~\widetilde{J}-tangent affine hyperspheres of an arbitrary dimension with an involutive distribution $\…

2018-04-04abs ↗pdf ↗

The paper examines stability of ReLU networks in tangent space and activation regions.

problem Stability and sensitivity of ReLU networks to small changes.
method Tangent sensitivity measure for ReLU networks, focusing on stability induced by individual examples.
result Tangent sensitivity correlates with the distribution of activation regions and generalization gap.

A subbundle of variable dimension inside the tangent bundle of a smooth manifold is called a smooth distribution if it is the pointwise span of a family of smooth vector fields. We prove that all such distributions are finitely generated, meaning that the family may be taken to be a finite collection. Further, we show …

2010-12-27abs ↗pdf ↗

We analyze the eigenvalue distribution of a neural network's kernel under specific scaling.

problem Analyzing the eigenvalue distribution of the Neural Tangent Kernel (NTK) of a neural network.
method Asymptotic analysis of the NTK matrix under given scaling conditions.
result The eigenvalue distribution is described as a free multiplicative convolution of the Marchenko-Pastur distribution and a deterministic distribution.

Neural Tangents is a library designed to enable research into infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit. Infinite-wid…

2019-12-05abs ↗pdf ↗

Analytic curves have infinite codimension of singular germs.

problem Understanding the codimension of singular tangent curves in analytic distributions.
method Formalizing asymptotic statements about finite jets of tangent curves and applying the h-principle.
result The subspace of singular germs has infinite codimension within smooth curves.

This study examines the practical equivalence of Laplace and neural tangent kernels.

problem Understanding the practical equivalence of Laplace and neural tangent kernels.
method The study matches the kernels exactly and by matching posteriors of a Gaussian process. It also analyzes the kernels in R^d and experiments with them in regression tasks.
result The Laplace and neural tangent kernels are practically equivalent.

We give an algebraic/geometric characterization of the classical pseudodifferential operators on a smooth manifold in terms of the tangent groupoid and its natural R+×\mathbb{R}^\times_+-action. Specifically, we show that a properly supported semiregular distribution on M×MM\times M is the Schwartz kernel of a classical …

2015-11-02abs ↗pdf ↗

It is well known that a k-dimensional smooth surface in a Euclidean space cannot be tangent to a non-involutive distribution of k-dimensional planes. In this paper we discuss the extension of this statement to weaker notions of surfaces, namely integral and normal currents. We find out that integral currents behave to …

2019-07-17abs ↗pdf ↗

In this note we announce some results, due to appear in [2], [3], on the structure of integral and normal currents, and their relation to Frobenius theorem. In particular we show that an integral current cannot be tangent to a distribution of planes which is nowhere involutive (Theorem 3.6), and that a normal current w…

2017-05-28abs ↗pdf ↗

The paper extends distributions by singular curves, revealing structural equivalences.

problem Extending (3,6)(3, 6)-distributions using singular curves.
method Using data from singular curves, the paper extends (3,6)(3, 6)-distributions to higher rank distributions.
result The equivalence of classification problems for four extended distribution classes.

We study conditions for the integrability of the distribution defined on a regular Poisson manifold as the orthogonal complement (with respect to some (pseudo)-Riemannian metric) to the tangent spaces of the leaves of a symplectic foliation. Examples of integrability and non-integrability of this distribution are provi…

2004-05-08abs ↗pdf ↗

New construction shows VMRTs of unbendable curves can be Legendrian.

problem Characterize VMRTs of unbendable rational curves under contact structures.
method Used geometry of contact lines and symplectic geometry of distributions.
result VMRTs of Legendrian submanifolds can be realized.

Bayesian deep ensembles improve prediction accuracy in various settings.

problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.

Geometric framework analyzes bias in variational inference for posterior functionals.

problem Analyzing the bias of posterior functionals under variational approximations.
method Developed a geometric framework to evaluate the bias of posterior functionals using the variational tangent space.
result The leading-order bias of a posterior functional is determined by its component orthogonal to the variational tangent space.

The paper introduces a differentially private method for optimization on Riemannian manifolds.

problem Differential privacy in optimization constrained to Riemannian manifolds.
method Adding Gaussian noise to the Riemannian gradient on the tangent space, with privacy and utility guarantees.
result Privacy and utility guarantees for differentially private Riemannian optimization.

In this note, we consider a fixed vector field VV on S2S^2 and study the distribution of points which lie on the nodal set (of a random spherical harmonic) where VV is also tangent. We show that the expected value of the corresponding counting function is asymptotic to the eigenvalue with a leading coefficient that i…

2018-09-05abs ↗pdf ↗

This paper analyzes convergence of FL for neural networks using NTK.

problem Theoretical guarantees of FL for neural networks with explicit forms and multi-step updates are unexplored.
method FL-NTK framework for federated learning of ReLU neural networks trained by gradient descent.
result FL-NTK converges to a global-optimal solution at a linear rate with proper learning parameters.

We say that a distribution is harmonic if it is harmonic when considered as a section of a Grassmann bundle. We find new examples of harmonic distributions and show nonexistense of harmonic distrubutions on some Riemannian manifolds by two different approaches. Firstly, we lift distributions to the second tangent bundl…

2009-09-27abs ↗pdf ↗

A method to fix radius distortion in generative models on curved spaces.

problem Distortion in geodesic radius measurements across different charts on Riemannian manifolds.
method Radial Compensation (RC) adjusts the tangent-space base distribution to match the geodesic radius law, improving model stability and interpretability.
result RC ensures that the model's geodesic radius matches the intended distribution, improving numerical stability and curvature interpretation.

Study h-principles for non-integrable distributions on manifolds.

problem Existence and classification of maximally non-integrable distributions of derived length one.
method Introduced formal structures and used h-principles to discuss existence and classification.
result Discussed existence and classification of maximally non-integrable distributions of derived length one.

Given a smooth distribution D\mathscr{D} of mm-dimensional planes along a smooth regular curve γγ in Rm+n\mathbb{R}^{m+n}, we consider the following problem: to find an mm-dimensional rank-one submanifold of Rm+n\mathbb{R}^{m+n}, that is, an (m1)(m-1)-ruled submanifold with constant tangent space along the rulings, such …

2018-11-20abs ↗pdf ↗

Method improves simulation accuracy by mitigating distribution shift in hybrid systems.

problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.

Constructs polyhedral chains with prescribed tangent plane distributions.

problem Constructing polyhedral chains with specific tangent plane distributions.
method Explicit construction of polyhedral chains that approximate prescribed measures on Grassmannian.
result Polyconvexity is equivalent to quasiconvexity of associated Q-integrands under certain conditions.

A holomorphic Engel structure determines a flag of distributions WDE\mathcal{W}\subset \mathcal{D}\subset \mathcal{E}. We construct examples of Engel structures on C4\mathbf{C}^4 such that each of these distributions is hyperbolic in the sense that it has no tangent copies of C\mathbf{C}. We also construct two infinite…

2017-06-28abs ↗pdf ↗

For a compact riemannian manifold of negative curvature, the geodesic foliation of its unit tangent bundle is independent of the negatively curved metric, up to Holder bicontinuous homeomorphism. However, the riemannian metric defines a natural transverse measure to this foliation, the Liouville transverse measure, whi…

2002-03-11abs ↗pdf ↗

We determine a 2-codimensional CR-structure on the slit tangent bundle T0MT_0M of a Finsler manifold (M,F)(M, F) by imposing a condition regarding the almost complex structure ΨΨ associated to FF when restricted to the structural distribution of a framed ff-structure. This condition is satisfied when (M,F)(M, F) is of scal…

2013-04-11abs ↗pdf ↗

Study of singular curves in a specific type of hyperbolic distribution.

problem Characterizing singular curves in hyperbolic (4,7)(4, 7)-distributions.
method Introduced hyperbolic (4,7)(4, 7)-distributions of type C3C_3, described singular curves via prolongations.
result Completely described singular curves for hyperbolic (4,7)(4, 7)-distributions of type C3C_3.

This paper introduces tangent display maps to simplify tangent category theory.

problem The category of smooth manifolds does not admit all pullbacks, complicating tangent category theory.
method Develops tangent display maps as a special class of maps well-behaved with respect to pullbacks.
result Tangent display maps simplify previous work in tangent categories and provide a new way to define open subobjects.

In this paper, we propose an auto-encoder based generative neural network model whose encoder compresses the inputs into vectors in the tangent space of a special Lie group manifold: upper triangular positive definite affine transform matrices (UTDATs). UTDATs are representations of Gaussian distributions and can strai…

2019-01-28abs ↗pdf ↗