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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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6371,2751,9122,549 · Jun 202019922001200920172026
48 results for local over- and under-densities

EagleEye detects localized density anomalies in multivariate data.

problem Identifying signal events, regime changes, or model mismatch in scientific data.
method EagleEye pinpoints local over- and under-densities by assigning anomaly scores based on binary membership sequences and binomial null models.
result EagleEye can detect genuine local anomalies and estimate background purity.

This work tackles sequential data learning challenges by improving neural network robustness to non-iid distribution shifts.

problem Sequential data learning challenges, particularly non-iid distribution shifts across batches.
method Cramér-Rao-based regularization using Fisher Information Matrix to adapt to sequential covariate shifts.
result Achieves 19% accuracy improvement over state-of-the-art methods.

An important part of the classical theory of real or complex manifolds is the theory of (smooth, real analytic or complex analytic) vector bundles. With any vector bundle over a manifold (M,F) the sheaf of its (smooth, real analytic or complex analytic) sections is associated which is a locally free sheaf of F-modules,…

2011-10-18abs ↗pdf ↗

We define Radon transform and its inverse on the two-dimensional anti-de Sitter space over local fields using a novel construction through a quadratic equation over the local field. We show that the holographic bulk reconstruction of quantum fields in this space can be formulated as the inverse Radon transform, general…

2018-05-18abs ↗pdf ↗

Paper classifies pseudomanifolds over stratified spaces.

problem Classifying pseudomanifolds over stratified spaces.
method Introducing locally standard TT-pseudomanifolds and using characteristic data.
result Locally standard TT-pseudomanifolds over topological stratified pseudomanifolds are classified by their characteristic data.

Investigate local Lie group structure of bisections over compact manifolds

problem Study the local Lie group structure associated with the space of admissible bisections of a local Lie groupoid over a compact manifold.
method Investigate the relation of this local Lie group to the Lie algebra of sections of the associated Lie algebroid.
result Prove that the globalizability of a local Lie groupoid implies the globalizability of its associated local Lie group of bisections.

Local convergence theory for mildly over-parameterized neural nets.

problem Understanding why over-parameterization works in neural networks.
method Developed a local convergence theory for two-layer neural nets, showing neuron convergence under certain conditions.
result All student neurons converge to one of teacher neurons when the loss is below a threshold.

Let AA be a finite-dimensional local commutative algebra over RR, dimRA=n\dim_RA=n. In this work we consider compact manifolds over AA, and prove that the real part of an AA-differentiable function is constant. Also we find estimates for the dimensions of some spaces of 1-form.

2004-02-14abs ↗pdf ↗

In this paper, we analyze the effects of depth and width on the quality of local minima, without strong over-parameterization and simplification assumptions in the literature. Without any simplification assumption, for deep nonlinear neural networks with the squared loss, we theoretically show that the quality of local…

2018-11-20abs ↗pdf ↗

Proposes a continuous, differentiable model from local adaptive models.

problem Inadequate continuity and differentiability in over-parameterized models.
method A global continuous and differentiable model constructed from weighted averages of locally learned models.
result Achieves faster statistical convergence and improved performance in various settings.

LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.

problem Optimizing large, complex design spaces is infeasible and unnecessary.
method LES uses Bayesian optimization to target solutions reachable by iterative optimizers.
result LES achieves strong sample efficiency compared to existing methods.

Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel loc…

2017-11-22abs ↗pdf ↗

A new framework enhances generative modeling by learning local flows over complex manifolds.

problem Limited expressivity of current normalizing flows for low-dimensional manifolds.
method Vector quantized local normalizing flows (VQ-Flows) using a VQ-AE atlas and conditional flows.
result Enhanced modeling of complex data distributions over manifolds.

We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each node of the network uses a convex optimization based algorithm that provides a locally optimum soluti…

2018-03-31abs ↗pdf ↗

The study examines non-Kähler threefolds with specific metrics and finds they are quasi-bundles over surfaces.

problem Understanding non-Kähler threefolds with algebraic dimension two.
method Examining compact complex non-Kähler threefolds with locally conformally Kähler metrics and proving they are quasi-bundles over projective surfaces under certain assumptions.
result They are blown-up quasi-bundles over a projective surface.

Paper shows faster convergence to local-minimizers in over-parametrized models under interpolation-like conditions.

problem Escaping saddle-points in over-parametrized models.
method Stochastic and deterministic optimization algorithms under interpolation-like conditions.
result Oracle complexity of PSGD and SCRN algorithms to reach εε-local-minimizer matches or improves upon deterministic rates.

Paper addresses FL over MAC with DP constraints, proposing a novel consensus scheme.

problem Federated learning over a multiple access channel with differential privacy constraints.
method Proposes a novel consensus scheme using digital distributed stochastic gradient descent (D-DSGD) with artificial noise to preserve DP.
result Demonstrates improved convergence rate and DP level for a given MAC capacity.

We complete the quasi-isometric classification of irreducible lattices in semisimple Lie groups over nondiscrete locally compact fields of characteristic zero by showing that any quasi-isometry of a rank one S-arithmetic lattice in a semisimple Lie group over nondiscrete locally compact fields of characteristic zero is…

2005-04-11abs ↗pdf ↗

Graphs can be smoothed or squashed too, study finds.

problem Graph Neural Networks struggle with over-smoothing and over-squashing issues.
method Unified framework using Ollivier-Ricci curvature to address both issues.
result Over-smoothing and over-squashing linked to positive and negative graph curvature respectively.

Improves multi-objective learning by adapting to local subintervals.

problem Learning a predictor satisfying multiple objectives in an online, changing data setting.
method Adapting an existing multi-objective learning method with an adaptive online algorithm.
result Improves predictions over subgroups and remains robust under distribution shift.

We study an infinite dimensional ASD moduli space over the cylinder. Our main result is the formula of its local mean dimension. A key ingredient of the argument is the notion of non-degenerate ASD connections. We develop its deformation theory and show that there exist sufficiently many non-degenerate ASD connections …

2013-02-25abs ↗pdf ↗

Distributed optimization often consists of two updating phases: local optimization and inter-node communication. Conventional approaches require working nodes to communicate with the server every one or few iterations to guarantee convergence. In this paper, we establish a completely different conclusion that each node…

2019-06-14abs ↗pdf ↗

A new method boosts graph neural networks by preventing over-smoothing and over-squashing.

problem Graph Neural Networks struggle with long-range signals and over-smoothing/over-squashing.
method Proposes PowerEmbed, a layer-wise normalization technique inspired by spectral graph embedding.
result PowerEmbed prevents over-smoothing and avoids over-squashing, improving performance on heterophilous graphs.

Paper proves stability of positive mass theorem for specific types of manifolds.

problem Stability of positive mass theorem for compact graphical manifolds.
method Used Federer--Fleming flat distance and static quasi-local Brown-York energy.
result Proved stability of positive mass theorem for compact (locally) hyperbolic graphical manifolds.

PF-LaCG removes the need for knowing smoothness and strong convexity parameters for locally accelerated CG.

problem Locally accelerated CG requires knowledge of smoothness and strong convexity parameters.
method Parameter-Free Locally Accelerated CG (PF-LaCG) algorithm.
result PF-LaCG achieves local acceleration without requiring knowledge of smoothness and strong convexity parameters.

A local monotonicity formula for the Yang-Mills-Higgs flow on GG-bundles over Rn\mathbb{R}^{n} (n>4n>4) is proved. It is shown that the monotone quantity coïncides on certain self-similar solutions with that appearing in existing non-local monotonicity formulæ for the Yang-Mills and Yang-Mills-Higgs flows.

2015-06-05abs ↗pdf ↗

Proposes a framework to incorporate global sensitivity into local surrogate models.

problem Narrowing focus to local scale in surrogate modeling leads to re-learning global trends.
method Integrates global sensitivity analysis into local surrogate models through input warping.
result Local models become equally sensitive to all input directions, focusing on local dynamics.

The paper extends local h-principles to complex structures on Stein manifolds.

problem Existence of local h-principles for complex structures on Stein manifolds.
method Introducing realifications of partial holomorphic relations and proving h-principles for them.
result Local h-principles can be extended to complex structures on Stein manifolds.