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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.

168,932 papers · 148 categories

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48 results for universal mapping

Unified method for CNNs to approximate equivariant maps across various groups.

problem Limited universal approximation theorems for CNNs with specific groups and settings.
method Unified approach to derive universal approximation theorems for equivariant maps by CNNs in diverse settings.
result Ability to handle non-linear equivariant maps between infinite-dimensional spaces for non-compact groups.

Generalizes neural network approximation to infinite-dimensional manifolds and derivatives.

problem Approximating differentiable maps on infinite-dimensional manifolds.
method Proves a weighted Nachbin theorem to establish universal approximation for differentiable maps, including derivatives.
result Linear functions of the signature can approximate path space functionals including their derivatives.

Paper establishes rates of universal approximation for neural tangent kernels using transport mappings.

problem Universal approximation for neural tangent kernels with microscopic weight changes.
method Generic scheme to approximate functions with NTK using transport mappings, constructed via Fourier transforms.
result Approximation of continuous functions with roughly 1 / δ^(10d) nodes, where δ depends on function continuity.

Mathematical proof of S-duality and universal isometries in q-map spaces.

problem Understanding the isometries of q-map spaces and their relation to S-duality.
method Purely mathematical proof of the existence of an SL(2,R)-action on q-map spaces, describing interactions with existing isometries.
result Presentation of a (3n+6)-dimensional group of universal isometries, enlarging previous results.

Narasihman and Ramanan proved that an arbitrary connection in a vector bundle over a base space B can be obtained as the pull-back (via a correctly chosen classifying map from B into the appropriate Grassmannian) of the universal connection in the universal bundle over the Grassmannian. The purpose of this paper is to …

2014-05-27abs ↗pdf ↗

This paper proves neural networks can approximate any infinite-dimensional map with uniform guarantees.

problem Universal approximation of infinite-dimensional maps by neural networks with uniform guarantees.
method Analysis of various infinite analogues of neural networks and their approximation capabilities.
result Any continuous map can be approximated arbitrarily closely by some infinite neural networks with mild topological conditions.

The aim of this paper is to introduce a group containing the mapping class groups of all genus zero surfaces. Roughly speaking, such a group is intended to be a discrete analogue of the diffeomorphism group of the circle. One defines indeed a {\it universal mapping class group of genus zero}, denoted $\B$. The latter i…

2002-10-01abs ↗pdf ↗

Quantum machine learning models can approximate any continuous function.

problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.

Universal approximation for ODENet and ResNet with a single activation function.

problem Approximating complex dynamical systems with limited vector fields.
method Examined ODENet and ResNet with vector fields composed of a single activation function and affine mapping.
result ODENet and ResNet with restricted vector fields can uniformly approximate those with general vector fields.

We consider compact, aspherical solenoids obtained as the inverse limit of a system of CW~complexes and covering maps. This includes PP-adic solenoids, as well as the universal hyperbolic solenoid of Teichmüller theory. Using ideas from shape theory, we classify maps between such solenoids up to homotopy, and we prove…

2010-09-28abs ↗pdf ↗

NODEs can approximate a wide range of diffeomorphisms with strong guarantees.

problem The approximation power of NODEs under certain conditions.
method Leveraging a structure theorem of the diffeomorphism group.
result NODEs can approximate a large class of diffeomorphisms with a stronger guarantee.

Path signatures adapted for Lie groups improve action recognition in computer vision.

problem Improving action recognition in computer vision with geometric constraints.
method Lifting path signatures to Lie groups and proving universality and characteristic property.
result Path signatures on Lie groups provide comparable performance to shallow learning approaches in action recognition.

Let G be a connected Lie group with Lie algebra g. The Duflo map is a vector space isomorphism between the symmetric algebra S(g) and the universal enveloping algebra U(g) which, as proved by Duflo, restricts to a ring isomorphism from invariant polynomials onto the center of the universal enveloping algebra. The Duflo…

1999-03-09abs ↗pdf ↗

New method creates universal perturbations to fool neural network interpretations.

problem Vulnerability of gradient-based saliency maps to adversarial perturbations.
method Gradient-based optimization and PCA-based approach to create UPI.
result Existence and successful application of Universal Perturbation for Interpretation (UPI).

FedRec learns universal receivers for fading channels without channel statistics.

problem Training neural network-based receivers for diverse fading channels without accurate statistics.
method Federated learning of a MAP detector for downlink fading channels.
result Performance approaches MAP without channel statistics, reduced communication overhead.

This article introduces a universal moduli space for the set whose archetypal element is a pair that consists of a metric and second fundamental form from a compact, oriented, positive genus minimal surface in some hyperbolic 3-manifold. This moduli space is a smooth, finite dimensional manifold with canonical maps to …

2004-10-13abs ↗pdf ↗

Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. This dimension reduction procedure succeeds when it preserves certain …

2015-11-30abs ↗pdf ↗

Chirped sinosoids and interferometric phase plots are functions that are not periodic, but are the composition of a smooth function and a periodic function. These functions functions factor into a pair of maps: from their domain to a circle, and from a circle to their codomain. One can easily imagine replacing the circ…

2015-01-25abs ↗pdf ↗

Method maps state space using landmarks for universal goal reaching.

problem Learning the Universal Value Function Approximator (UVFA) for long-range goals is challenging.
method Hierarchical modeling with a dynamic landmark-based map and a value network.
result The method enables agents to reach long-range goals at the early training stage.

We prove that the mapping class group Γg,nΓ_{g,n} for surfaces of negative Euler characteristic has a cofinite universal space $\E$ for proper actions (the resulting quotient is a finite CWCW-complex). The approach is to construct a truncated Teichmueller space $\T_{g,n}(ε)$ by introducing a lower bound for the length of…

2008-11-24abs ↗pdf ↗

In "Rips complexes and covers in the uniform category" \cite{Rips} the authors define, following James \cite{J}, covering maps of uniform spaces and introduce the concept of generalized uniform covering maps. Conditions for the existence of universal uniform covering maps and generalized uniform covering maps are given…

2010-08-02abs ↗pdf ↗

We study the geometry of the twistor space of the universal hyperkaehler implosion Q for SU(n). Using the description of Q as a hyperkaehler quiver variety, we construct a holomorphic map from the twistor space Z_Q of Q to a complex vector bundle over P^1, and an associated map of Q to the affine space R of the bundle'…

2013-08-14abs ↗pdf ↗

Deep learning networks are approximated using dynamical systems theory.

problem Understanding the approximation capabilities of deep learning networks.
method Modeling deep residual networks as continuous-time dynamical systems and using approximation theories in LpL^p.
result Established general sufficient conditions for universal approximation of deep residual networks.

Enhances generative models by improving expressivity without high computational cost.

problem Improving expressivity in generative models without increasing computational complexity.
method Proposes a new family of generative flows on an augmented data space, proving they can approximate a Hamiltonian ODE as a universal transport map.
result Demonstrates state-of-the-art performance on flow-based generative modeling benchmarks.

The goal of this article is to give an elementary introduction to Dirac geometry and group-valued moment maps, via pure spinors. The material is based on my lectures at the summer school on 'Poisson geometry in Mathematics and Physics' at Keio University, June 2006.

2006-09-11abs ↗pdf ↗