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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.

169,341 papers · 148 categories

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57114170227 · Jun 202619922001200920182026
48 results for piecewise-linear manifolds

Hilbert initiated the standpoint in foundations of mathematics. From this standpoint, we allow only a finite number of repetitions of elementary operations when we construct objects and morphisms. When we start from a subset of a Euclidean space. Then we assume that any element of the line has only a finite number of c…

2013-10-21abs ↗pdf ↗

This paper studies PL cobordism categories and their homotopy types.

problem Understanding the homotopy types of PL cobordism categories.
method Introducing a bordism category and showing weak homotopy equivalence to an infinite loop space.
result The classifying space BCdPLB\mathcal{C}_d^{PL} is weak homotopy equivalent to an infinite loop space.

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.

The paper approximates smooth isotropic surfaces with piecewise linear ones.

problem Approximating smooth isotropic surfaces with piecewise linear ones.
method Using analogies with infinite dimensional moment map geometry, the authors prove the approximation of smooth isotropic immersions by piecewise linear ones.
result Smooth isotropic immersions can be approximated by piecewise linear isotropic maps.

Piecewise-linear approximation improves feature selection in logit models.

problem Improving feature subset selection in sequential logit models.
method Applied piecewise-linear approximation to logistic loss function to frame feature selection as a mixed integer linear optimization problem.
result Piecewise-linear approximation found a better subset of features than quadratic approximation.

Piecewise linear activations create many spurious local minima in neural networks.

problem Understanding the loss surface of neural networks with piecewise linear activations.
method Proved the existence of infinite spurious local minima and partitioned the loss surface into smooth cells.
result Piecewise linear activations create many spurious local minima that are invariant under a continuous path.

Piecewise-linear regression trees improve tree-based regression with theoretical and practical benefits.

problem Improving tree-based regression models with theoretical guarantees and practical tractability.
method Regularized piecewise-linear node-splitting criterion, LASSO-type and 2\ell_{2} regularization, variable selection procedure.
result New high-probability generalization error bounds for piecewise-linear regression trees.

New approach treats neural networks with piecewise linear activations using tropical geometry.

problem Upper bounds on linear regions of neural networks with ReLU or leaky ReLU activations.
method Treat neural network layers with piecewise linear activations as tropical polynomials, refining upper bounds using tropical geometry.
result Upper bounds on linear regions improved to $\min\left\{ 2^m, \sum_{j=0}^n \binom{m}{j} ight\}$, where n,mn, m are the number of inputs and outputs, respectively.

Paper presents ABGD for efficient piecewise linear regression in high dimensions.

problem Efficiently solving piecewise linear regression in high-dimensional spaces.
method Parametrizes piecewise linear functions as difference of max-affine functions, using ABGD algorithm.
result ABGD converges linearly to an ε-accurate estimate with optimal sample complexity.

Here are versions of the proofs of two classic theorems of combinatorial topology. The first is the result that piecewise linearly homeomorphic simplicial complexes are related by stellar moves. This is used in the proof, modelled on that of Pachner, of the second theorem. This states that moves from only a finite coll…

1999-11-20abs ↗pdf ↗

New proof shows efficient ReLU networks for piecewise linear functions.

problem Existence of efficient ReLU neural networks for piecewise linear functions.
method Degree 1 triangulations of the relative homology class bounded by polyhedra.
result Existence of efficient ReLU neural networks for functions with compact support.

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.

The paper tackles prescribing discrete Gaussian curvature on polyhedral surfaces.

problem Prescribing discrete Gaussian curvature on polyhedral surfaces.
method Discrete conformal theory and variational principles with constraints.
result Proves Kazdan-Warner type theorems for polyhedral surfaces.

We prove Csorba's conjecture that the Lovász complex Hom(C_5,K_n) of graph multimorphisms from the 5-cycle C_5 to the complete graph K_n is Z/2Z-equivariantly homeomorphic to the Stiefel manifold, V(n-1,2), the space of (ordered) orthonormal 2-frames in R^{n-1}. The equivariant piecewise-linear topology that we need is…

2013-02-12abs ↗pdf ↗

A hybrid model combines piecewise linear and neural components for interpretable predictions.

problem Post-hoc interpretable methods lead to contradictory explanations and lower prediction accuracy.
method Hybrid model with piecewise linear and neural components.
result The model achieves good interpretability and state-of-the-art accuracy.

The paper proves a theorem about higher-dimensional expansion and its topological implications.

problem Higher-dimensional expansion properties and their topological consequences.
method Detailed proof of Gromov's Topological Overlap Theorem using cellular cochains and simplicial complexes.
result The theorem states that if a complex has strong higher-dimensional expansion properties, it has a topological overlap property.

New method uses DC functions for piecewise linear regression.

problem Regression with piecewise linear constraints.
method Estimates piecewise linear convex functions using a difference of convex functions.
result Method achieves close to minimax statistical risk and comparable performance to existing methods.

A triangulated piecewise-linear minimal surface in Euclidean 3-space defined using a variational characterization is critical for area amongst all continuous piecewise-linear variations with compact support that preserve the simplicial structure. We explicitly construct examples of such surfaces that are embedded and a…

2004-10-13abs ↗pdf ↗

The paper extends a variance gamma model to quadratic functions, reducing arbitrage and computational costs.

problem Creating an arbitrage-free interpolation for option pricing models.
method Generalizing the local variance gamma model to a piecewise quadratic local variance function.
result The quadratic model results in an arbitrage-free interpolation of class C3, reducing knots and computational cost.

A new complexity measure for neural networks improves upon classical methods.

problem Lack of a refined complexity measure for comparing different neural network architectures, especially permutation-invariant ones.
method Introduced an equivalence relation among linear functions and counted them relative to this relation.
result The new complexity measure clearly distinguishes between different models and increases exponentially with depth.

LinXGBoost extends XGBoost for better regression of piecewise linear functions.

problem Regression of functions with jumps or discontinuities is challenging.
method LinXGBoost stores linear models at each leaf, equivalent to piecewise regularized least-squares.
result LinXGBoost outperforms vanilla XGBoost and Random Forest in experiments.

Simplifies PLNNs to interpretable models for better explainability.

problem Challenges in interpretability of PLNNs for high-stakes applications.
method Trained deep network simplification and algorithm for reducing flat networks.
result Improved interpretability of PLNNs without sacrificing performance.

We exhibit a knot PP in the solid torus, representing a generator of first homology, such that for any knot KK in the 3-sphere, the satellite knot with pattern PP and companion KK is not smoothly slice in any homology 4-ball. As a consequence, we obtain a knot in a homology 3-sphere that does not bound a piecewise-…

2014-05-06abs ↗pdf ↗