NWoS solves high-dimensional Poisson equations using neural networks.
problem Efficiently solving high-dimensional Poisson equations.
method Neural Walk-on-Spheres (NWoS) leveraging stochastic representations and Walk-on-Spheres methods.
result NWoS outperforms competing methods in accuracy, speed, and computational costs.
Spheres' spectral structure converges to Gaussian space's as dimensions grow.
problem Understanding spectral convergence between high-dimensional spheres and Gaussian spaces.
method Proving spectral convergence using projections and eigenvalues.
result Spectral structure on high-dimensional spheres converges to Gaussian space's as dimensions increase.
The study constructs and shows isotopy of high-dimensional Legendrian spheres.
problem Understanding Legendrian spheres in contact manifolds of any dimension.
method Three Legendrian sphere constructions using open books and a doubling procedure.
result These constructions are isotopic to the Legendrian unknot.
Minimal triangulations of spheres map almost linearly to boundaries of high-dimensional polytopes.
problem Finding the minimum number of vertices for triangulations of spheres that map to high-dimensional boundaries.
method Analyzing triangulations of n-spheres and their maps to boundaries of (n+1)-simplexes, focusing on h=⌊2n+1floor. result The function λ(n,d)h is almost linear in d as do∞. We investigate the existence of homotopy comoment maps (comoments) for high-dimensional spheres seen as multisymplectic manifolds. Especially, we solve the existence problem for compact effective group actions on spheres and provide explicit constructions for such comoments in interesting particular cases.
New method explains high-dimensional sphere data with latent factors.
problem Understanding intricate dependence structure in high-dimensional sphere data.
method Exploratory factor analysis of the projected normal distribution with a fast alternating expectation profile conditional maximization algorithm.
result Uniformly excellent results on various data types, including tweets, brain imaging, and cancer gene expression.
The paper analyzes high-dimensional sphere solutions to the Nirenberg problem with residual mass.
problem The Nirenberg problem on high-dimensional spheres with residual mass.
method Analysis of subcritical approximations and blowing up solutions.
result Comprehensive description of blowing up solutions, including blow-up points and rates.
Conditions for integer signatures of high-dimensional knots.
problem Determining signatures of high-dimensional knots with specific Alexander polynomials.
method Necessary and sufficient conditions based on square-free Alexander polynomials.
result Identifies conditions for an integer to be the signature of a knot.
Variational Auto-Encoder (VAE) has been widely applied as a fundamental generative model in machine learning. For complex samples like imagery objects or scenes, however, VAE suffers from the dimensional dilemma between reconstruction precision that needs high-dimensional latent codes and probabilistic inference that f…
We present a rigidity theorem for the action of the mapping class group π0(Diff(M)) on the space R+(M) of metrics of positive scalar curvature for high dimensional manifolds M. This result is applicable to a great number of cases, for example to simply connected 6-manifolds and high dimensi…
A simple geometrical proof shows that any target function can be found in a random network's neighborhood.
problem Finding any target function in a random network's neighborhood.
method Geometrical proof using a simple model of a high-dimensional sphere projected onto a low-dimensional subspace.
result High-dimensional geometry ensures that a uniform distribution over a sphere reduces to a Gaussian distribution with negligible covariances, enabling the presence of any target function in a random network's neighborhood.
A classical result of Milman roughly states that every Lipschitz function on Sn is almost constant on a sufficiently high-dimensional sphere Sm⊂Sn. In this paper we extend the result by proving that any Lipschitz function on a positively curved homogeneous space is almost consta…
New MCMC methods map high-dimensional problems to spheres for better mixing.
problem Mixing issues in high-dimensional distributions, especially heavy-tailed ones.
method Stereographic Markov Chain Monte Carlo (MCMC) methods that map high-dimensional problems to spheres.
result Uniformly ergodic samplers for various distributions, including heavy-tailed ones, with faster convergence in higher dimensions.
Two-parameter models can learn high-dimensional targets via gradient flow.
problem Learning high-dimensional targets with limited parameters.
method Gradient flow approach for W<d models. result Two-parameter models can learn targets with arbitrarily high success probability.
We prove a homological stability theorem for moduli spaces of high-dimensional, highly connected manifolds, with respect to forming the connected sum with the product of spheres Sp×Sq, for p<q<2p−2. This result is analogous to recent results of S. Galatius and O. Randal-Williams regarding the homo…
SRCA reduces high-dimensional data to lower dimensions while preserving geometric structures.
problem High-dimensional datasets with underlying geometric structures.
method Spherical Rotation Component Analysis (SRCA) incorporating geometric loss functions.
result SRCA provides a low-rank spherical representation of data with general theoretic guarantees.
Proves triviality of inertia groups in high-dimensional manifolds.
problem Classifying manifolds in the metastable range.
method Understanding the second extended power functor in synthetic spectra.
result Inertia groups of high-dimensional manifolds are trivial.
Similar simplices can be inscribed in most smoothly embedded spheres.
problem Inscribing families of similar simplices in spheres.
method Diffeomorphic mapping and techniques from previous work on inscribing triangles.
result A dense family of spheres allows inscribing similar simplices of every pose.
The paper solves the Nirenberg problem on high-dimensional half spheres with pinching conditions.
problem Finding conformal metrics of prescribed scalar curvature and zero boundary mean curvature on half spheres.
method Variational approach with pseudogradient and Morse theory to handle non-compactness.
result Existence results for the Nirenberg problem under various pinching conditions.
We consider pairs (X,Y) where X is a compact, locally CAT(-1) space, and Y is a totally geodesic subspace. The inclusion induces an embedding of the boundaries at infinity of the universal covers; we focus on the case where these are spheres whose dimensions differ by 2. We show that if the embedding is tame, then it i…
We define Pin(2)-equivariant Seiberg-Witten Floer homology for rational homology 3-spheres equipped with a spin structure. The analogue of Froyshov's correction term in this setting is an integer-valued invariant of homology cobordism whose mod 2 reduction is the Rokhlin invariant. As an application, we show that there…
We study the topology of the space of positive scalar curvature metrics on high dimensional spheres and other spin manifolds. Our main result provides elements of infinite order in higher homotopy and homology groups of these spaces, which, in contrast to previous approaches, are of infinite order and survive in the (o…
We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed …
We show that, for certain families φs of diffeomorphisms of high-dimensional spheres, the commutator of the Dehn twist along the zero-section of T∗Sn with the family of pullbacks φs∗ gives a noncontractible family of compactly-supported symplectomorphisms. In particular, we find example…
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…
New simplicial complexes show unavoidable link of spheres in high dimensions.
problem Finding unavoidable links of spheres in high-dimensional spaces.
method Simple argument in piecewise linear topology and application of the van Kampen--Flores theorem.
result Existence of additional simplicial complexes with unavoidable links of spheres.
Paper proves embedding theorem for conformally compact manifolds.
problem Embedding conformally compact manifolds into hyperbolic spaces.
method Proves analogous Nash Embedding Theorem for conformally compact manifolds.
result Conformally compact manifolds can be isometrically embedded into hyperbolic spaces.
New families of embeddings in 4-manifolds, topologically trivial but smoothly non-trivial.
problem Constructing non-trivial smooth embeddings of 3-manifolds in 4-manifolds.
method Parameterized families of embeddings, using high-dimensional spheres.
result Embeddings of homology spheres and any 3-manifold in blown-up K3 surfaces.
The homotopy theory of gauge groups has received considerable attention in recent decades. In this work, we study the homotopy theory of gauge groups over some high dimensional manifolds. To be more specific, we study gauge groups of bundles over (n−1)-connected closed 2n-manifolds, the classification of which was …
We use classical results in smoothing theory to extract information about the rational homotopy groups of the space of negatively curved metrics on a high dimensional manifold. It is also shown that smooth M-bundles over spheres equipped with fiberwise negatively curved metrics, represent elements of finite order in th…
New method solves PDEs on spheres using physics-informed convolutional neural networks.
problem Solving PDEs on surfaces, especially spheres, with high accuracy and efficiency.
method Physics-informed convolutional neural networks (PICNN) with theoretical analysis and approximation results.
result Established fast convergence rates for PICNN solving PDEs on spheres.
While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors. In this manuscript we present a novel framework that uses the inverted index representation to exploit struct…
Ancient solutions arise in the study of Ricci flow singularities. Motivated by the work of Fateev on 3-dimensional ancient solutions we construct high dimensional ancient solutions to Ricci flow on spheres and complex projective spaces as well as the twistor spaces over a compact quaternion-Kahler manifold. Differing f…
A new Gaussian process regression method infers implicit manifold structure from data.
problem Scaling Gaussian process regression to high-dimensional data.
method Proposes a fully differentiable Gaussian process regression technique that infers implicit manifold structure from data.
result Improves predictive performance and calibration of standard Gaussian process regression in high-dimensional settings.
GDMaps reduces high-dimensional data to lower dimensions for better classification.
problem High-dimensional data classification and representation.
method Grassmannian Diffusion Maps technique for nonlinear dimensionality reduction.
result GDMaps effectively identifies intrinsic subspace structures in high-dimensional data.
Completes classification of high-dimensional manifolds up to exotic sums.
problem Classifying high-dimensional manifolds up to exotic sums.
method Algebraic refinement of intersection forms and Witten genus.
result All but finitely many dimensions are resolved, completing the classification.
Reflective Hamiltonian Monte Carlo struggles with high-dimensional sampling.
problem Slow mixing in reflective Hamiltonian Monte Carlo with inexact reflections.
method Quantifying instantaneous non-uniformity with Sinkhorn divergence; analyzing particle motion in spheres and cubes; constructing low-dimensional toy models.
result Particles spontaneously unmix, leading to resonances in particle density.
The hypercube's perimeter is significantly larger than expected near half volume.
problem Understanding the isoperimetric profile of the hypercube.
method Analytical proof of perimeter bounds and comparison to Gaussian isoperimetric profile.
result The isoperimetric profile of the hypercube does not converge to the Gaussian profile as dimension increases.
New insights into simple kernel smoothing reveal surprising asymptotics.
problem Understanding precise asymptotics of Nadaraya-Watson kernel smoothing.
method Using ideas from the random energy model in statistical physics.
result Sharp asymptotics for the NW predictor on the sphere.
The study examines how gamma positivity and PL homeomorphism types affect simplicial spheres.
problem Understanding gamma positivity and its relation to PL homeomorphism types in simplicial spheres.
method Using edge contractions and the link condition as proxies for flagness, the study analyzes the effect of gamma positivity on simplicial spheres.
result The link condition has a trivial effect on gamma vectors of high-dimensional simplicial spheres with nonnegative gamma vectors.
New formula for curvatures of curves in n-dimensional space.
problem Calculating curvatures of curves in high-dimensional spaces.
method Explicit formula for curvatures using derivatives.
result Generalization of Pappus' theorems to higher dimensions.
Classifies certain high-dimensional manifolds with specific cohomology properties.
problem Classifying smooth manifolds with a specific cohomology structure.
method Analyzes cohomology rings and uses topological equivalences.
result Classifies manifolds up to diffeomorphism, homeomorphism, and homotopy equivalence.
Contact homology for Legendrian submanifolds in standard contact (2n+1)-space is rigorously defined using moduli spaces of holomorphic disks with Lagrangian boundary conditions in complex n-space. It provides new invariants of Legendrian isotopy. Using these invariants the theory of Legendrian isotopy is shown to b…
Analyzes optimal learning rate schedules in high-dimensional non-convex optimization problems.
problem Optimizing high-dimensional non-convex loss landscapes.
method Langevin optimization with learning rate decaying as \(η(t) = t^{-β}\).
result To speed up optimization without getting stuck in saddles, a decay rate \(β < 1\) is optimal, contrary to convex setups where \(β = 1\).
Gradient descent solves rank-one matrix estimation problem with detailed time evolution analysis.
problem Estimating a rank-one symmetric matrix corrupted by noise.
method Gradient descent on a sphere, using local versions of the semi-circle law.
result Explicit formulas for the time evolution of the estimator and cost function, revealing phase transitions.
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scaling (MDS) method, where instead of performing gradient descent, we sample and evaluate possible "moves" in a sphere of fixed radius for each …
A novel method relaxes binary constraints to non-negative spheres for multi-matching and clustering.
problem Optimization problems over binary matrices with injectivity constraints.
method Non-negative spherical relaxation followed by conditional power iteration.
result Automatic adjustment of the continuous parameter related to universe size.
Study learns a projection and function in Gaussian models.
problem Learning a one-dimensional projection and a univariate function in high-dimensional Gaussian models.
method Gradient flow dynamics of alternating scheme, RKHS adaptation.
result Gradient flow dynamics converge with rate controlled by Gaussian regularity.