Research
On-device research index

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.

169,051 papers · 148 categories

Trend · papers per month

25.0%50.0%75.0%100.0% · Sep 199219922001200920182026
48 results for generalized linear mappings

S. Bigelow proved that the braid groups are linear. That is, there is a faithful representation of the braid group into the general linear group of some field. Using this, we deduce from previously known results that the mapping class group of a sphere with punctures and hyperelliptic mapping class groups are linear. I…

2000-10-27abs ↗pdf ↗

The paper provides a concentration result and sample complexity for linear Monge mapping estimation and its application in domain adaptation.

problem Estimating the linear Monge mapping between distributions and its application in domain adaptation.
method The approach involves proving a concentration result and sample complexity for the linear mapping operator, and using it to derive a generalization bound for domain adaptation with optimal transport.
result The method achieves a sample complexity of n1/2n^{-1/2} and approaches the performance of theoretical Bayes predictor under mild conditions.

Characterizes a general range decreasing group homomorphism.

problem Understanding range decreasing group homomorphisms in the entire mapping group.
method Characterization of a general range decreasing group homomorphism.
result Computes a particular class of homomorphisms and identifies all range decreasing group homomorphisms on specific mapping groups.

We define compactifications of vector spaces which are functorial with respect to certain linear maps. These "many-body" compactifications are manifolds with corners, and the linear maps lift to b-maps in the sense of Melrose. We derive a simple criterion under which the lifted maps are in fact b-fibrations, and identi…

2017-12-11abs ↗pdf ↗

\infty-Harmonic maps are a generalization of \infty-harmonic functions. They can be viewed as the limiting cases of p-harmonic maps as p goes to infinity. In this paper, we give complete classifications of linear and quadratic \infty-harmonic maps from and into a sphere, quadratic \infty-harmonic maps between E…

2007-10-30abs ↗pdf ↗

Study linearizes 2-Wasserstein space using optimal transport maps.

problem Stability and linearization of the 2-Wasserstein space.
method Explicit embedding of probability measures into a Hilbert space using optimal transport maps.
result The embedding is (bi-)Hölder continuous, with stability results for optimal transport maps.

Equivariant neural networks use symmetry to interpret complex data.

problem Interpreting and understanding the behavior of equivariant neural networks.
method Decompose layers into simple representations and analyze nonlinear activation functions.
result Equivariant neural networks can be interpreted using a filtration generalizing Fourier series.

Classifies non-linear Fredholm maps linking to stable homotopy groups of spheres.

problem Classifying non-linear proper Fredholm maps between Hilbert spaces.
method Using stable homotopy groups of spheres to classify maps up to proper homotopy.
result Determines the non-trivial kernel of the map from stable homotopy groups to non-linear proper Fredholm maps.

In his celebrated paper "Generic projections", John Mather has shown that almost all linear projections from a submanifold of a vector space into a subspace are transverse with respect to a given modular submanifold. In this paper, an improvement of Mather's result is stated. Namely, we show that almost all linear pert…

2016-07-12abs ↗pdf ↗

Study classifies hypersurfaces in 4D Lorentz-Minkowski space using specific operators.

problem Classifying tubular hypersurfaces in 4D Lorentz-Minkowski space.
method Analysis of Gauss map and linearized operators L1\mathcal{L}_{1} and L2\mathcal{L}_{2}.
result Classifications of hypersurfaces with specific types of Gauss maps.

Note on subgaussian bounds for sign-quantized linear maps.

problem Understanding subgaussian behavior of sign-quantized linear maps.
method Developed a dimension-independent subgaussian concentration bound for Gaussian vectors under nonlinear mappings.
result Answered a question about sign-quantized linear maps using a new subgaussian bound.

The paper generalizes equivariant neural networks on homogeneous spaces to the non-linear setting.

problem Equivariant neural networks on homogeneous spaces.
method Deriving generalized steerability constraints for non-linear equivariant layers.
result The universality of the derived construction for non-linear equivariant layers.

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.

Proves surjectivity of certain smooth maps with non-properness sets.

problem Surjectivity of linear operators and global diffeomorphisms of semialgebraic maps.
method Analytic proof involving CC^{\infty} semialgebraic local diffeomorphisms and linear partial differential operators.
result A new analytic conjecture for polynomial local diffeomorphisms of RnR^n implies known results.

Study of hyperelliptic mapping class groups with applications and profinite completions.

problem Understanding hyperelliptic mapping class groups and their properties.
method Defined and studied hyperelliptic mapping class groups, applied theory to counterexamples, and examined profinite completions.
result Found a counterexample to a conjecture about mapping class groups and extended congruence subgroup property.

Biharmonic maps between surfaces are studied in this paper. We compute the bitension field of a map between surfaces with conformal metrics in complex coordinates. As applications, we show that a linear map from Euclidean plane into (R2,σ2dwdwˉ)(\mathbb{R}^2, σ^2dwd\bar w) is always biharmonic if the conformal factor σσ is bi-a…

2010-08-04abs ↗pdf ↗

Open and discrete maps with specific branch set images are equivalent to PL branched covers.

problem Understanding the equivalence of open and discrete maps and PL branched covers.
method Demonstrated that an open and discrete map f ⁣:SnoSnf \colon \mathbb{S}^n o \mathbb{S}^n with a specific branch set image is equivalent to a PL branched cover up to homeomorphism.
result Open and discrete maps with a specific branch set image are equivalent to PL branched covers.

We study the space of "link maps": the space of maps of a disjoint union of compact, closed manifolds P_1, . . ., P_k into a manifold N whose images are pairwise disjoint. We apply the manifold calculus of functors developed by Goodwillie and Weiss to study the difference between it and its linear and quadratic approxi…

2007-02-06abs ↗pdf ↗

We obtain a criterion for approximability by embeddings of piecewise linear maps of a circle to the plane, analogous to the one proved by Minc for maps of a segment to the plane. Theorem. Let S be a triangulation of a circle with s vertices. Let f be a simplicial map of the graph S to the plane. The map f is approximab…

2008-08-08abs ↗pdf ↗

In this paper, we give complete classifications of linear \infty-harmonic maps between Euclidean and Heisenberg spaces, between Nil and Sol spaces. We also classify all \infty-harmonic linear endomorphisms of Sol space and show that there is a subgroup of \infty-harmonic linear automorphisms in the group of linea…

2007-11-05abs ↗pdf ↗

Paper proposes linear transformers for efficient in-context learning without context length limitations.

problem Quadratic complexity of softmax transformers limits data processing speed.
method Investigates linear transformers under domain generalization, showing they learn mappings from context distributions to response functions.
result Linear transformers achieve in-context learning with a linear complexity in context length, offering a dimension-independent convergence rate.