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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.

168,742 papers · 148 categories

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67135202269 · Jun 202019922001200920172026
48 results for explainable maps

New feature mapping approach improves recommendation accuracy and explainability.

problem Balancing recommendation accuracy and explainability using metadata.
method Maps uninterpretable features to interpretable aspect features, minimizing both prediction and interpretation losses.
result Strong performance in recommendation and explainability, eliminating metadata need.

1-Lipschitz neural networks produce clearer, more focused Saliency Maps for explainable AI.

problem Noisy and limited Saliency Maps from traditional neural networks.
method Dual loss of optimal transport problem for 1-Lipschitz neural networks.
result Saliency Maps from 1-Lipschitz networks are highly concentrated and less noisy, aligning with human explanations.

This paper reviews MDS, Sammon mapping, and Isomap, explaining their theory and applications.

problem Exploring multidimensional data structures and mappings.
method Explains classical MDS, metric MDS, kernel classical MDS, Sammon mapping, Isomap, and their applications.
result Detailed understanding of MDS, Sammon mapping, and Isomap methods.

FCDD explains deep anomaly detection by mapping anomalies away and providing heatmap explanations.

problem Deep one-class classification's non-linear transformation makes it hard to interpret.
method FCDD learns a mapping that concentrates nominal samples, maps anomalies away, and provides heatmap explanations.
result FCDD sets a new state of the art in unsupervised anomaly detection on MVTec-AD.

The study reveals simplicity bias in neural networks leading to better compositional mappings.

problem Understanding when and how to encourage neural networks to learn compositional mappings.
method Examined compositional mappings through coding length and gradient descent dynamics.
result Neural networks tend to learn the simplest bijections, explaining their good generalization.

This survey studies equivariant harmonic maps arising from Higgs bundles. We explain the non-abelian Hodge correspondence and focus on the role of equivariant harmonic maps in the correspondence. With the preparation, we review current progress towards some open problems in the study of equivariant harmonic maps.

2018-09-15abs ↗pdf ↗

WassersteinGrad improves weather forecasting explanations by addressing geometric misalignment issues.

problem Improving explainability of autoregressive neural predictions on dynamic physical fields.
method WassersteinGrad, a geometric consensus method for averaged perturbed attribution maps.
result WassersteinGrad provides more accurate explanations for weather forecasting models.

Study evaluates saliency maps on artificial data with different backgrounds.

problem Objective evaluation of saliency methods on artificial data with varying backgrounds.
method Developed a framework to generate artificial data with synthetic lesions and a known ground truth map, evaluated two data sets with different backgrounds (Perlin noise and 2D brain MRI slices).
result Heatmaps vary strongly between saliency methods and backgrounds.

We establish a connection between recent developments in the study of vortices in the abelian Higgs models, and in the theory of structure-preserving discrete conformal maps. We explain how both are related via conformal mapping problems involving prescribed linear combinations of the curvature and volume form, and sho…

2017-03-14abs ↗pdf ↗

We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key properties: completeness and weak dependence, which provably cann…

2019-05-02abs ↗pdf ↗

We study Dirac structures associated with Manin pairs (\d,\g) and give a Dirac geometric approach to Hamiltonian spaces with D/G-valued moment maps, originally introduced by Alekseev and Kosmann-Schwarzbach in terms of quasi-Poisson structures. We explain how these two distinct frameworks are related to each other, pro…

2007-10-02abs ↗pdf ↗

Improves machine learning models by incorporating physical laws into feature maps.

problem Lack of model interpretability in classical machine learning approaches.
method Physics-informed feature maps constructed from physical laws and dimensional analysis.
result Enhanced model interpretability and potential discovery of new physical equations.

This paper classifies typhoon damage features using aerial photography.

problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.

Sharp bounds found for energy in projective space mappings.

problem Finding bounds for energy in mappings of real projective spaces.
method Sharp lower and upper bounds for energy in homotopy classes of mappings from real projective space to Riemannian manifolds.
result Characterization of maps that achieve the lower bound for energy and determination of the infimum of energy in a homotopy class.

In this paper we study the modular classes of Dirac manifolds and of Dirac maps, and we discuss their basic properties. We apply these results to explain the relationship between the modular classes of the various structures involved in the reduction of a Poisson manifold under the action by of a Poisson Lie group.

2016-01-26abs ↗pdf ↗

The Teichmüller harmonic map flow, introduced in [9], evolves both a map from a closed Riemann surface to an arbitrary compact Riemannian manifold, and a constant curvature metric on the domain, in order to reduce its harmonic map energy as quickly as possible. In this paper, we develop the geometric analysis of holomo…

2012-09-17abs ↗pdf ↗

A conformal map from a Riemann surface to a Euclidean space of dimension greater than or equal to three is explained by using the Clifford algebra, in a similar fashion to quaternionic holomorphic geometry of surfaces in the Euclidean three- or four-space. The Weierstrass representation, the spin transform, the Darboux…

2017-07-23abs ↗pdf ↗

We explain how the formal aspects of the theory of Kahler-Einstein metrics can be developed in the framework of moment maps. The central result we use is the Berndtsson convexity theorem, which is interpreted as defining a metric on the space of complex structures. We discuss some applications of these ideas to the Kah…

2015-03-17abs ↗pdf ↗

Survey of Laplacian-based methods for data dimensionality reduction and embedding.

problem Efficiently reducing high-dimensional data to lower dimensions while preserving important features and structures.
method Laplacian-based methods including spectral clustering, Laplacian eigenmap, locality preserving projection, graph embedding, and diffusion map.
result Comprehensive overview of various optimization variants and applications of Laplacian-based techniques.

Local decision boundary approximation improves model explanations for complex models.

problem Challenges in explaining complex, opaque machine learning models.
method Train a variational autoencoder to learn a latent space and map it to meaningful attributes. Use these attributes to approximate the local decision boundary and explain model predictions.
result Can recover latent attributes that determine class decisions in a new benchmark data set.

This article has two purposes. The first is to give an expository account of the integrable systems approach to harmonic maps from surfaces to Lie groups and symmetric spaces, focusing on spectral curves for harmonic 2-tori. The most unwieldy aspect of the spectral curve description is the periodicity conditions and th…

2012-11-13abs ↗pdf ↗

New method for explaining neural network activation functions.

problem Transparency in black-box deep learning algorithms.
method Symbolic explanation of activation functions using adaptive Gaussian Processes.
result Achieved partially explainable learning model with scalable topology.

New findings show mapping class groups of certain high-dimensional manifolds are not residually finite.

problem Understanding the mapping class groups of simply connected high-dimensional manifolds.
method Provided a counterexample showing mapping class groups are not residually finite.
result Mapping class groups of some high-dimensional manifolds are not residually finite.

Notes on quasiregular maps between Riemannian manifolds, preserving Sobolev forms.

problem Extending quasiregular map theory from Euclidean to Riemannian manifolds.
method Recalling different approaches to first-order Sobolev spaces, showing equivalence, and transferring key theorems.
result Pull-backs with quasiregular maps preserve Sobolev differential forms of the conformal exponent.

Kontsevich's formula for a deformation quantization of Poisson structures involves a Feynman series of graphs, with the weights given by some complicated integrals (using certain pullbacks of the standard angle form on a circe). We explain the geometric meaning of this series as degrees of maps of some grand configurat…

2002-10-07abs ↗pdf ↗

Two modified tests improve the reliability of evaluating explanation methods.

problem Methodological concerns in evaluating explanation methods for saliency maps.
method Proposed modifications to the Model Parameter Randomisation Test (MPRT): Smooth MPRT and Efficient MPRT.
result Enhanced metric reliability, facilitating more trustworthy deployment of explanation methods.