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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.

168,694 papers · 148 categories

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76152227303 · May 202619922001200920172026
48 results for smoothed normalization

New algorithm tackles nonconvex machine learning problems with adaptive normalization and independent sampling.

problem Nonconvex machine learning problems with generalized-smoothness.
method Adaptive gradient normalization, independent sampling, and gradient clipping.
result Achieves an O(ε^(-4)) sample complexity for fast convergence.

In this article, we shall investigate the relationship between the existence or non-existence of non-singular solutions to the normalized Ricci flow and smooth structures on closed 4-manifolds, where non-singular solutions to the normalized Ricci flow are solutions which exist for all time t[0,)t \in [0, \infty) with unif…

2008-07-14abs ↗pdf ↗

Article studies symmetry in smooth vector bundles using advanced operations.

problem Symmetry phenomena in smooth vector bundles after two iterations of the normal functor.
method Developed theory of pullback and quotient for double vector bundles and morphisms, focusing on naturality of the normal functor.
result Expected symmetry is obtained through universal behavior and compatibility of operations.

We prove the existence of a local smooth Levi decomposition for smooth Poisson structures and Lie algebroids near a singular point. In the appendix of this paper, we show an abstract Nash-Moser normal form theorem, which generalizes our Levi decomposition result and which may be helpful in the study of other smooth nor…

2002-09-01abs ↗pdf ↗

Yau's Affine Normal Descent optimizes smooth unconstrained problems with geometrically adapted directions.

problem Optimizing smooth unconstrained problems with geometrically adapted directions.
method Yau's Affine Normal Descent (YAND) uses the equi-affine normal of level-set hypersurfaces as search directions.
result YAND converges globally under standard smoothness assumptions and locally quadratically near nondegenerate minimizers.

The study proves that in normal tilings, at least two vertices are required per cell.

problem Understanding the minimum number of vertices required in normal tilings.
method The research examines both periodic and monohedral tilings in 2D, proving the minimum number of non-smooth vertices required.
result The study confirms that for normal tilings, at least two vertices are necessary per cell.

Stochastic approximation proves asymptotic normality for non-smooth problems.

problem Solving non-smooth stochastic approximation problems.
method Stochastic approximation algorithms for solving smooth equations, extended to non-smooth problems.
result Asymptotic normality and optimality in non-smooth stochastic approximation is proven.

A new technique normalizes nodes within groups to improve GNN performance.

problem Over-smoothing in deeper GNNs reduces node distinguishability.
method Differentiable group normalization (DGN) to separate node distributions among groups.
result DGN makes GNN models more robust to over-smoothing and achieves better performance with deeper GNNs.

New algorithm for differentially private distributed optimization of smooth, non-convex problems.

problem No differentially private distributed method for smooth, non-convex optimization problems.
method Smoothed normalization integrated with an error-feedback mechanism.
result Achieves superior convergence rate and first differentially private distributed optimization algorithm with provable convergence guarantees.

A new proof shows almost every normal to a smooth convex body intersects at least 6 normals from different points.

problem The conjecture about normals to convex bodies in high dimensions.
method Short proof of Y. Martinez-Maure's result for n3n \geq 3.
result Almost every normal through a boundary point intersects at least 6 normals from different points.

Defines weak normals for irregular curves in high-dimensional spaces.

problem Dealing with irregular curves in high-dimensional Euclidean spaces.
method Using sequences of inscribed polygonals and Gram-Schmidt procedure, introduces a relaxed notion of weak normals.
result Weak normals for irregular curves are the strong limit of approximating polygonals and agree with relaxed energy.

We investigate a method of construction of Calabi--Yau manifolds, that is, by smoothing normal crossing varieties. We develop some theories for calculating the Picard groups of the Calabi--Yau manifolds obtained in this method. Some applications are included, such as construction of new examples of Calabi--Yau 3-folds …

2006-04-27abs ↗pdf ↗

Paper explores whether gradient normalization can replace clipping for SGD in heavy-tailed noise.

problem Ensuring convergence of SGD in heavy-tailed noise.
method Revisits gradient clipping and normalization, proving their sufficiency and effectiveness.
result Gradient normalization alone is sufficient for nonconvex SGD convergence under smoothness assumptions.

In this paper we complete the study of the normal holonomy groups of complex submanifolds (non nec. complete) of Cn or CPn. We show that irreducible but non transitive normal holonomies are exactly the Hermitian s-representations of [CD09, Table 1] (see Corollary 1.1). For each one of them we construct a non necessaril…

2015-03-06abs ↗pdf ↗

Efficient EP algorithm improves smoothing distribution inference in financial models.

problem Computational intractability of smoothing distribution in high dimensions.
method Adapted expectation propagation (EP) algorithms for the unified skew-normal family.
result Accuracy gains in financial illustrations over existing approximate algorithms.

In this paper we prove that the Kähler-Einstein metrics for a degeneration family of Kähler manifolds with ample canonical bundles Gromov-Hausdorff converge to the complete Kähler-Einstein metric on the smooth part of the central fiber when the central fiber has only normal crossing singularities inside smooth total sp…

2003-03-10abs ↗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 ↗

Extends normalizing flows to arbitrary smooth manifolds.

problem Current normalizing flows are limited to basic geometries and cannot handle complex real-world data.
method Uses Neural ODEs and geometric control theory to extend flows to arbitrary smooth manifolds.
result Demonstrates scalable unbiased estimator for divergence in generalized setting.

Generalizes Poincaré-Hopf Theorem for piecewise smooth boundaries.

problem Conservation law for vector fields on surfaces with piecewise smooth boundaries.
method Generalization of the Poincaré-Hopf Theorem for real-analytic vector fields on surfaces with piecewise smooth boundaries.
result Conservation law for vector fields on surfaces with piecewise smooth boundaries.

Researchers relax the CVF's smoothness requirement to create more flexible flow models.

problem Challenges in constructing flexible density models due to the CVF's smoothness requirement.
method Introduce L\mathcal{L}-diffeomorphisms as generalized transformations that may violate smoothness on zero Lebesgue-measure sets.
result The relaxation allows for the use of non-smooth activation functions like ReLU in residual flows.

Study on algebraic curves' invariants and vanishing criteria.

problem Vanishing criteria for Griffiths infinitesimal invariants of algebraic curves.
method Analysis of moduli space of smooth genus 4 curves, study of normal functions.
result Vanishing criteria for the Griffiths infinitesimal invariants of Ceresa normal function.

The paper strengthens the classical result of MLE convergence to a Gaussian distribution.

problem The classical result of MLE convergence to a Gaussian distribution.
method Sub-Gaussian concentration and entropic normality of the normalized MLE.
result Entropic central limit theorem for a smoothed version of the estimator.

The paper proves a theorem and characterizes connections over normal varieties.

problem The study addresses the stability and connections over normal varieties.
method The authors prove a complete version of the Donaldson-Uhlenbeck-Yau theorem and use it to show the polystability of reflexive sheaves.
result An admissible Hermitian-Yang-Mills connection defines a polystable reflexive sheaf and gives a lower bound for discriminants.

Normal forms for equivariant maps in infinite dimensions established.

problem Establishing normal forms for equivariant maps in infinite-dimensional manifolds.
method Inspired by Lyapunov-Schmidt reduction and Kuranishi method, uses Slice Theorem for Fréchet manifolds.
result Abstract moduli spaces of equivariant maps are locally modeled on quotient by a compact group.

New optimizers control network width scaling, improving stability and transfer across different model sizes.

problem Designing stable optimizers for networks of varying widths.
method Interpreting optimizers as steepest descent under mean-normalized operator norms, enabling layerwise composability and width-independent bounds.
result New optimizers like row normalization and column normalization provide stable learning-rate transfer across different model widths.

This paper presents a tensor multiplication based smoothing algorithm that follows a two step denoising method. Unlike other traditional averaging approaches, our approach uses an element based normal voting tensor to compute smooth surfaces. By introducing a binary optimization on the proposed tensor together with a l…

2016-07-20abs ↗pdf ↗

Gradient descent on normalized networks reveals sparsity preferences.

problem Understanding the inductive bias of gradient descent on normalized neural nets.
method Analysis of gradient descent on weight-normalized smooth homogeneous neural nets, focusing on SWN and EWN.
result EWN causes weights to be updated in a way that prefers asymptotic relative sparsity.

We give a comprehensive theoretical characterization of a nonparametric estimator for the L22L_2^2 divergence between two continuous distributions. We first bound the rate of convergence of our estimator, showing that it is n\sqrt{n}-consistent provided the densities are sufficiently smooth. In this smooth regime, we t…

2014-10-30abs ↗pdf ↗

For an arbitrary Riemannian manifold XX and Hermitian vector bundles EE and FF over XX we define the notion of the normal symbol of a pseudodifferential operator PP from EE to FF. The normal symbol of PP is a certain smooth function from the cotangent bundle TXT^*X to the homomorphism bundle Hom(E,F)Hom (E,F) and dep…

1996-12-11abs ↗pdf ↗