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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.

168,742 papers · 148 categories

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130260390520 · Jun 202019922001200920172026
48 results for weighted points

Method identifies change points in high-dimensional models using sample weights.

problem Identifying change points in high-dimensional generalized linear models.
method Sample-weighted empirical risk minimization (Weighted ERM).
result Weighted ERM yields precise asymptotic performance characterization for Gaussian designs.

Weil-Petersson volumes vary continuously with weighted points on a projective line.

problem Continuity of Weil-Petersson volumes in moduli space with weighted points.
method Localization and geometric computation methods.
result CM volume converges to geometric volume as weights approach Calabi-Yau geometry.

Neural networks' weights don't converge to stationary points but training loss stabilizes.

problem The disconnect between theoretical analyses and neural network training practice.
method An invariant measure perspective inspired by ergodic theory of dynamical systems.
result The distribution of weights converges to an approximate invariant measure, explaining loss stabilization.

New method calculates Ricci curvature from distances between weighted volumes.

problem Calculating Ricci curvature for weighted Riemannian manifolds.
method Asymptotic retrieval of generalized Ricci tensor from scaled metric derivatives of Wasserstein 1-distances.
result Limiting coarse curvature of random graphs converges to generalized Ricci tensor.

The classical kk-means algorithm for partitioning nn points in Rd\mathbb{R}^d into kk clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been observed in many scientific and business applications. In this paper, we present an…

2013-08-19abs ↗pdf ↗

Generative model uses random weighted support points for interpretable data sampling.

problem Creating diverse and interpretable sample sets from large datasets efficiently.
method Random weighted support points from Dirichlet process and Bayesian bootstrap.
result High-quality and diverse outputs at lower computational cost.

We prove by Hilbert-Mumford criterion that a slope stable polarized weighted pointed nodal curve is Chow asymptotic stable. This generalizes the result of Caporaso on stability of polarized nodal curves, and of Hasset on weighted pointed stable curves polarized by the weighted dualizing sheaves. It also solved a questi…

2011-08-08abs ↗pdf ↗

The conjecture of Kosniowski asserts that if the circle acts on a compact unitary manifold MM with a non-empty fixed point set and MM does not bound a unitary manifold equivariantly, then the dimension of the manifold is bounded above by a linear function on the number of fixed points. We confirm the conjecture for a…

2018-12-29abs ↗pdf ↗

Enhances physics-informed neural networks with adaptive sampling and weighting.

problem Challenges in training physics-informed neural networks on complex problems.
method Hybrid adaptive sampling and weighting method.
result Consistently improves prediction accuracy and training efficiency.

Given a weighted line arrangement in the projective plane, with weights satisfying natural constraint conditions, we show the existence of a Ricci-flat Kähler metric with cone singularities along the lines asymptotic to a polyhedral Kähler cone at each multiple point. Moreover, we discuss a Chern-Weil formula that expr…

2017-12-21abs ↗pdf ↗

A recent analysis of a model of iterative neural network in Hilbert spaces established fundamental properties of such networks, such as existence of the fixed points sets, convergence analysis, and Lipschitz continuity. Building on these results, we show that under a single mild condition on the weights of the network,…

2019-08-16abs ↗pdf ↗

A weight system on graph homology was constructed by Rozansky and Witten using a compact hyperkähler manifold. A variation of this construction utilizing holomorphic vector bundles over the manifold gives a weight system on chord diagrams. We investigate these weights from the hyperkähler geometry point of view.

2000-02-25abs ↗pdf ↗

We improve deep threshold networks' memorization capacity exponentially.

problem Memorizing datasets with randomized labels using deep neural networks.
method Using Gaussian random weights in the first layer and binary or integer weights in subsequent layers, we prove a new dependence on minimum distance.
result We show that O~(1δ+n)\widetilde{\mathcal{O}}(\frac{1}{\delta} + \sqrt{n}) neurons and O~(dδ+n)\widetilde{\mathcal{O}}(\frac{d}{\delta} + n) weights are sufficient.

The paper analyzes neural network dynamics after weights escape the origin.

problem Understanding gradient flow dynamics of neural networks after the origin.
method Analyzes gradient flow of homogeneous neural networks with locally Lipschitz gradients.
result Characterizes the first saddle point encountered after escaping the origin.

We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on linear discriminant analysis and provides Bayes optimal single point estimates for the weight values.

2014-06-12abs ↗pdf ↗

In this paper, we bound the error induced by using a weighted skeletonization of two data sets for computing a two sample test with kernel maximum mean discrepancy. The error is quantified in terms of the speed in which heat diffuses from those points to the rest of the data, as well as how at the weights on the refere…

2018-12-11abs ↗pdf ↗

Study on a weighted Suita conjecture for higher derivatives and their geometric properties.

problem Analyzing the Suita conjecture for higher derivatives with weights.
method Examining the set of points for equality in a weighted Suita conjecture and relating it to harmonic functions and Dirichlet problems.
result Relations between the set of points and integer-valued points of harmonic functions and Dirichlet problems for planar domains.

Study resolves polynomial germs, proving no mixed critical points and strict transform properties.

problem Resolving mixed critical points and properties of strict transforms of polynomial germs.
method Toric resolutions and modifications of weighted homogeneous polynomials.
result No mixed critical points and strict transform properties as germs.

Post-training quantization method using multiple low-precision points achieves higher precision for critical weights.

problem Discretizing pre-trained deep neural networks without re-training.
method Multipoint quantization with efficient greedy selection and adaptive point number.
result Outperforms state-of-the-art methods on ImageNet classification and PASCAL VOC object detection.

We develop the theory of weighted Ricci curvature in a weighted Lorentz-Finsler framework and extend the classical singularity theorems of general relativity. In order to reach this result, we generalize the Jacobi, Riccati and Raychaudhuri equations to weighted Finsler spacetimes and study their implications for the e…

2019-08-11abs ↗pdf ↗

New proof for 6D symplectic manifold with 4 fixed points.

problem Classifying the integral cohomology ring and total Chern class for 6D symplectic manifolds with 4 fixed points.
method New different argument using moment map values and weights of fixed points.
result Determined the sets of weights and global invariants for the manifold.

In this paper we study the functional $\SW_{λ_1,λ_2}$, which is the the sum of the Willmore energy, λ1λ_1-weighted surface area, and λ2λ_2-weighted volume, for surfaces immersed in R3\R^3. This coincides with the Helfrich functional with zero `spontaneous curvature'. Our main result is a complete classification of all …

2012-01-22abs ↗pdf ↗

This paper improves forecast stability without sacrificing accuracy using dynamic loss weighting.

problem Rolling origin forecast instability in time series forecasting.
method Dynamic loss weighting algorithms applied to the N-BEATS model.
result Dynamic loss weighting can further improve forecast stability without compromising accuracy.

Utilizing a weight matrix we study surfaces of prescribed weighted mean curvature which yield a natural generalisation to critical points of anisotropic surface energies. We first derive a differential equation for the normal of immersions with prescribed weighted mean curvature, generalising a result of Clarenz and vo…

2007-11-15abs ↗pdf ↗

Novel metric space magnitude and weighting vectors improve machine learning tasks.

problem Improving machine learning algorithms using novel metric space concepts.
method Metric space magnitude and weighting vectors for better machine learning.
result The weighting vector effectively detects boundaries and improves classic machine learning tasks.

Algorithm selects variables and bandwidths for geographically weighted regression.

problem Estimating variable subsets and bandwidths for geographically weighted regression.
method Mathematical programming-based approach integrating variable selection and bandwidth estimation.
result Proposed algorithm provides stable spatially varying patterns with competitive explanatory power.

In the paper arXiv:1411.4887 [math.AP] it is shown that the set of Riemannian metrics which do not admit global limiting Carleman weights is open and dense, by studying the conformally invariant Weyl and Cotton tensors. In the paper arXiv:1011.2507 [math.DG] it is shown that the set of Riemannian metrics which do not a…

2015-09-07abs ↗pdf ↗

V1 cortex reconstructs images as Poisson equation solutions with varying weights.

problem Reconstructing images from V1 cortical cell receptive profiles.
method Solves a heterogeneous Poisson equation with varying weights representing neural connectivity.
result Reconstructions converge to homogeneous solutions using homogenization techniques.

The paper studies neural networks' convergence near origin and saddle points.

problem Directional convergence of neural networks near small initializations and saddle points.
method Gradient flow dynamics analysis of two-homogeneous neural networks.
result Neural networks' weights approximately converge in direction to KKT points for small initializations.

Study circle actions on unitary manifolds with discrete fixed points.

problem Understanding circle actions on compact unitary manifolds with discrete fixed points.
method Prove relationships between weights at fixed points and derive results regarding the first equivariant Chern class and Hirzebruch χyχ_y-genus.
result Derive a multigraph encoding fixed point data, leading to new insights into unitary S1S^1-manifolds.

Fermat constants fail to fully identify Clairaut constants for certain geodesics on a surface of revolution.

problem Identifying Clairaut constants from Fermat constants for specific geodesics.
method Analytical proof for a specific class of geodesics on a surface of revolution.
result Fermat constants do not fully determine Clairaut constants for some geodesics, except for a standard sphere.

Global inducing points improve Bayesian neural network performance.

problem Improving Bayesian neural network performance.
method Adapting correlated approximate posterior to all layers in a Bayesian neural network and deep Gaussian processes using learned global inducing points.
result State-of-the-art performance on CIFAR-10 (86.7%) without data augmentation or tempering.

Adaptive weights improve physics-informed neural networks and deep operator networks.

problem Training physics-informed neural networks and deep operator networks can be challenging, leading to unsatisfactory accuracy and efficiency.
method Proposes a pointwise adaptive weighting method that balances the residual decay rate across different training points.
result Our proposed approach of balanced residual decay rates offers advantages including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

The paper aims at proving global height estimates for Killing graphs defined over a complete manifold with nonempty boundary. To this end, we first point out how the geometric analysis on a Killing graph is naturally related to a weighted manifold structure, where the weight is defined in terms of the length of the Kil…

2016-12-05abs ↗pdf ↗

The paper calculates a specific weight system for chord diagrams with a particular graph structure.

problem Calculating a specific weight system for chord diagrams with a complete bipartite graph structure.
method Using a Lie algebra sl3\mathfrak{sl}_3 and its weight system, the authors derive a function on chord diagrams.
result The authors compute the sl3\mathfrak{sl}_3 weight system for chord diagrams with a complete bipartite graph structure.

Optimizes weights for better model performance in shifting data.

problem Improper importance weighting leads to poor model performance in data shifts.
method Interprets weights as a bias-variance trade-off and optimizes them simultaneously with model parameters.
result Optimizing weights significantly improves model generalization performance.