惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

N
Netflix TechBlog - Medium
博客园 - 三生石上(FineUI控件)
Martin Fowler
Martin Fowler
博客园 - 【当耐特】
雷峰网
雷峰网
宝玉的分享
宝玉的分享
IT之家
IT之家
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
博客园 - 叶小钗
V
Visual Studio Blog
Engineering at Meta
Engineering at Meta
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
月光博客
月光博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
美团技术团队
爱范儿
爱范儿
T
The Blog of Author Tim Ferriss
L
LangChain Blog
U
Unit 42
有赞技术团队
有赞技术团队
博客园_首页

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Discrete uniformizing metrics on distributional limits of...
James R. Lee · 2017-01-25 · via math.PR updates on arXiv.org

Suppose that $\{G_n\}$ is a sequence of finite graphs such that each $G_n$ is the tangency graph of a sphere packing in $\mathbb{R}^d$. Let $ρ_n$ be a uniformly random vertex of $G_n$ and suppose that $(G,ρ)$ is the distributional limit of $\{(G_n,ρ_n)\}$ in the sense of Benjamini and Schramm. Then the conformal growth exponent of $(G,ρ)$ is at most $d$. In other words, there exists a unimodular "unit volume" weighting of the graph metric on $(G,ρ)$ such that the volume growth of balls in the weighted path metric is bounded by a polynomial of degree $d$. This generalizes to limits of graphs that can be "coarsely" packed in an Ahlfors $d$-regular metric measure space. Using our previous work, this implies that, under moment conditions on the degree of the root $ρ$,the almost sure spectral dimension of $G$ is at most $d$. This fact was known previously only for graphs packed in $\mathbb{R}^2$ (planar graphs), and the case of $d > 2$ eluded approaches based on extremal length. In the process of bounding the spectral dimension, we establish that the spectral measure of $(G,ρ)$ is dominated by a variant of the $d$-dimensional Weyl law.