arXiv research

We prove that every piecewise linear manifold of dimension up to four on which a finite group acts by piecewise linear homeomorphisms admits a compatible smooth structure with respect to which the group acts smoothly. This solves a challenge posed by Thurston in dimension three and confirms a conjecture by Kwasik and L…

We study algebraic varieties of ReLU networks to understand their representable functions.

New applications of trace embedding lemma show exotic 4-manifolds properties.

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…

The paper tackles prescribing discrete Gaussian curvature on polyhedral surfaces.

A fundamental object in a hyperbolic 3-manifold M is its convex core C(M), defined as the smallest closed non-empty convex subset of M. We investigate the way the geometry of the boundary S of C(M) varies as we vary the hyperbolic metric of M. Thurston observed that the intrinsic metric of S is hyperbolic, and that its…

The center of a quotient group of piecewise linear homeomorphisms is trivial.

Study introduces combinatorial criterion for quasi-isometry groups of Euclidean spaces.

This article provides an attempt to extend concepts from the theory of Riemannian manifolds to piecewise linear spaces. In particular we propose an analogue of the Ricci tensor, which we give the name of an Einstein vector field. On a given set of piecewise linear spaces we define and discuss (normalized) Ricci flows. …

Solves asset allocation for investors with utility functions and limits.

Global approximation for piecewise linear paths via signatures.

PARC uses piecewise linear predictors for regression and classification.

Investigates stability of piecewise flat Ricci flow using analysis and simulations.

Simplifies PLNNs to interpretable models for better explainability.

Smooth symplectic manifolds can be approximated by PL symplectic manifolds.

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

This is a survey paper where we expose the Kirby--Siebenmann results on classification of PL structures on topological manifolds and, in particular, the homotopy equivalence TOP/PL=K(Z/2.3) and the Hauptvermutung for manifolds.

Signature uniquely identifies piecewise linear surfaces up to thin homotopy.

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

New framework interprets deep neural networks through input structure.

SyMPLER improves time series forecasting in nonstationary environments with explainable models.

This work generalizes bounds on the number of linear regions in CPWL NNs.

In this paper, we introduce a bordism category $\mathcal{C}_d^{PL}$ whose objects are bundles of closed $(d-1)$-dimensional piecewise linear manifolds and whose morphisms are bundles of $d$-dimensional piecewise linear cobordisms. In the main theorem of this article, we show that the classifying space $B\mathcal{C}_d^{…

Study shows efficient neural network approach for stochastic bandits.

First explicit isometric immersion of a flat Klein bottle in 3D space.

GraN-GAN normalizes gradients for better GAN performance.

Paper proposes variational inference for piecewise-linear systems.

Tropical geometry and weighted lattices improve curve and surface fitting.

New GP model estimates piecewise continuous functions.

New method uses DC functions for piecewise linear regression.

Equivariant neural networks use symmetry to interpret complex data.

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

Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficul…

We present a detailed analysis of the class of regression decision tree algorithms which employ a regulized piecewise-linear node-splitting criterion and have regularized linear models at the leaves. From a theoretic standpoint, based on Rademacher complexity framework, we present new high-probability upper bounds for …

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

Neural networks can represent complex piecewise functions efficiently.

We consider smooth isotropic immersions from the 2-dimensional torus into $R^{2n}$, for $n \geq 2$. When $n = 2$ the image of such map is an immersed Lagrangian torus of $R^4$. We prove that such isotropic immersions can be approximated by arbitrarily $C^0$-close piecewise linear isotropic maps. If $n \geq 3$ the piece…

New cones in 4D space found with minimal mass.

In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have raised concerns over their use in safety-critical applications. A first step to unde…

Proposes adaptive ridge regression for functional linear models with piecewise shapes.

New proof shows efficient ReLU networks for piecewise linear functions.

We characterize finite groups G generated by orthogonal transformations in a finite-dimensional Euclidean space V whose fixed point subspace has codimension one or two in terms of the corresponding quotient space V/G with its quotient piecewise linear structure.

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

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…

The Immersed Boundary (IB) method is a widely-used numerical methodology for the simulation of fluid-structure interaction problems. The IB method utilizes an Eulerian discretization for the fluid equations of motion while maintaining a Lagrangian representation of structural objects. Operators are defined for transmit…

XGBoost is often presented as the algorithm that wins every ML competition. Surprisingly, this is true even though predictions are piecewise constant. This might be justified in high dimensional input spaces, but when the number of features is low, a piecewise linear model is likely to perform better. XGBoost was exten…

We consider the generic regularized optimization problem $\hat{\mathsfβ}(λ)=\arg \min_βL({\sf{y}},X{\sfβ})+λJ({\sfβ})$. Efron, Hastie, Johnstone and Tibshirani [Ann. Statist. 32 (2004) 407--499] have shown that for the LASSO--that is, if $L$ is squared error loss and $J(β)=\|β\|_1$ is the $\ell_1$ norm of $β$--the opti…

We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models c…