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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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140281421561 · Jun 202019922001200920172026
48 results for regularity class

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, …

2018-09-25abs ↗pdf ↗

Regularization and data augmentation can be class-dependent, leading to poor performance on some classes.

problem Class-dependent effects of regularization and data augmentation.
method Evaluation of regularization and data augmentation techniques on Imagenet and INaturalist datasets.
result Regularization and data augmentation can lead to significant performance drops on some classes.

We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we propose a geometric regularization technique to find the submanifold corresponding to a robust estimator of the class probability P(yx)P(y|\pmb{x}). The regularization term meas…

2015-03-04abs ↗pdf ↗

We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data. We use the Wasserstein-2 geometry to capture semantically meaningful neighborhoods in the space of images, and define a corresponding input-dependent additive noise data augmentation model. …

2019-09-15abs ↗pdf ↗

Improved covariance matrix estimation for multiple classes with limited data.

problem Estimating covariance matrices for multiple classes with scarce data.
method Coupled regularized sample covariance matrix estimator (RSCM) that combines pooled SCM and scaled identity matrix for regularization.
result The coupled RSCM estimators outperform cross-validation in classification tasks with comparable accuracy but faster computation.

A fast method for discrete OT with group-sparse regularization for class label preservation.

problem Efficiently measuring the distance between two discrete distributions with class labels.
method Fast discrete OT with group-sparse regularizers using gradient-based algorithms.
result Up to 8.6 times faster than original method without degrading accuracy.

Determines regular homotopy classes for link immersions of simple singularities.

problem Classifying immersions of link singularities.
method Computing complete invariants of immersions and comparing with Dynkin diagrams.
result Inclusion map of link into 5-sphere is regularly homotopic to immersion associated with Dynkin diagram.

For any regular Courant algebroid, we construct a characteristic class a la Chern-Weil. This intrinsic invariant of the Courant algebroid is a degree-3 class in its naive cohomology. When the Courant algebroid is exact, it reduces to the Severa class (in H^3_{DR}(M)). On the other hand, when the Courant algebroid is a …

2009-09-02abs ↗pdf ↗

New algorithms for latent class analysis using regularized spectral clustering.

problem Identifying latent classes within populations from categorical data.
method Developed two new algorithms using a regularized Laplacian matrix to estimate latent classes.
result Our algorithms provide consistent latent class analysis under mild conditions and can accurately infer the number of latent classes.

New findings on mapping class group actions on the circle, improving critical regularity.

problem Improving understanding of mapping class group actions on the circle.
method Analyzing actions of non-solvable groups and finite index subgroups of mapping class groups.
result Critical regularity of mapping class groups is at most one for surfaces of complexity at least three.

Regularizing for or against class selectivity in DNNs improves test accuracy.

problem The necessity and sufficiency of class selectivity in DNNs.
method Direct regularization of class selectivity in convolutional neural networks.
result Reducing class selectivity improves test accuracy, while increasing it decreases it.

Study slice-regular polynomial functions via twistor space group actions.

problem Characterize slice-regular functions and their polynomial subclasses.
method Employ the twistor construction and group actions of PGL(2,H)\mathrm{PGL}(2,\mathbb{H}).
result Characterize slice-regular functions with planar twistor lifts and normal classes of polynomials.

Optimally regularizes boundaries in the Heisenberg group with prescribed curvature.

problem Optimizing boundaries with prescribed sub-Finsler mean curvature in the Heisenberg group.
method Analyzes critical sets of the prescribed mean curvature functional in the Heisenberg group.
result Characteristic curves of critical sets are C2C^2-regular, optimal in the Heisenberg group.

Deep networks adapt to function regularity and data distribution.

problem Understanding deep learning's adaptability to function regularity and data distribution.
method Developed nonparametric approximation and estimation theories for a broad class of functions using deep ReLU networks.
result Deep neural networks are adaptive to different regularity of functions and nonuniform data distributions.

Deep neural networks achieve optimal learning rates for high-dimensional classification.

problem Learning classification functions from noisy data with smooth boundaries.
method Empirical risk minimization over deep neural networks for locally Barron-regular decision boundaries.
result Optimal estimation rates are independent of dimension and can be achieved by deep neural networks.

This paper extends foliation concepts to singular foliations using Lie \infty-algebroids.

problem Cohomological obstruction to volume forms in singular foliations.
method Replacing singular foliations with universal Lie \infty-algebroids to define modular class.
result Geometric meaning of modular class as an obstruction to universal Lie \infty-algebroids.

Random feature approximation speeds up spectral methods and improves learning rates.

problem Improving the efficiency and generalization of spectral methods in large-scale algorithms.
method Combining random feature approximation with spectral regularization methods.
result Optimal learning rates for estimators over various regularity classes, including those not in the RKHS.

The study connects group structure to smooth actions on one-manifolds.

problem Understanding how group actions affect the smoothness of manifolds.
method Analyzes the relationship between group algebraic structure and smoothness of group actions on one-dimensional manifolds.
result Uniform construction of groups acting on compact interval and circle with prescribed regularity.

Study categorizes Vaisman manifolds with vanishing first Chern class and finds canonical metrics.

problem Characterizing Vaisman manifolds with vanishing first Chern class.
method Categorization into three types based on Bott-Chern class sign, showing canonical metrics, quasi-regularity, stability, and automorphism group behavior.
result Vaisman manifolds with non-positive Bott-Chern class admit canonical metrics and are stable under deformations.

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstei…

2019-04-08abs ↗pdf ↗

A new algorithm for faster model selection in twin multi-class SVM.

problem Challenges in effective solution of multi-classification and fast model selection in twin multi-class SVM.
method Sample data set partition strategy, Lagrangian multipliers, piecewise linear update, initialization algorithm, and event-based iteration.
result Comparable classification performance achieved without solving quadratic programming problems.

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.