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

M
MIT News - Artificial intelligence
WordPress大学
WordPress大学
GbyAI
GbyAI
S
SegmentFault 最新的问题
量子位
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
MyScale Blog
MyScale Blog
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
aimingoo的专栏
aimingoo的专栏
V
Visual Studio Blog
U
Unit 42
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
The Cloudflare Blog
Stack Overflow Blog
Stack Overflow Blog
博客园 - 聂微东
J
Java Code Geeks
The GitHub Blog
The GitHub Blog
Y
Y Combinator Blog
IT之家
IT之家
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
雷峰网
雷峰网

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Stiefel manifolds and upper bounds for spherical codes an...
Masoud Zargar · 2024-07-15 · via cs.IT updates on arXiv.org

We improve upper bounds on sphere packing densities and sizes of spherical codes in high dimensions. In particular, we prove that the maximal sphere packing densities $δ_n$ in $\mathbb{R}^n$ satisfy \[δ_n\leq \frac{1+o(1)}{e}\cdot δ^{\text{KL}}_{n}\] for large $n$, where $δ^{\text{KL}}_{n}$ is the best bound on $δ_n$ obtained essentially by Kabatyanskii and Levenshtein from the 1970s with improvements over the years. We also obtain the same improvement factor for the maximal size $M(n,θ)$ of $θ$-spherical codes in $S^{n-1}$: for angles $0<θ<θ'\leq\fracπ{2}$, \[M(n,θ)\leq \frac{1+o(1)}{e}\cdot \frac{M_{\text{Lev}}(n-1,θ')}{μ_n(θ,θ')}\] for large $n$, where $μ_n(θ,θ')$ is the mass of the spherical cap in the unit sphere $S^{n-1}$ of radius $\frac{\sin(θ/2)}{\sin(θ'/2)}$, and $M_{\text{Lev}}(n-1,θ')$ is Levenshtein's upper bound on $M(n-1,θ')$ when applying the Delsarte linear programming method to Levenshtein's optimal polynomials. In fact, we prove that there are no analytic losses in our arguments and that the constant $\frac{1}{e}=0.367...$ is optimal for the class of functions considered. Our results also show that the improvement factor does not depend on the special angle $θ^*=62.997...^{\circ}$, explaining the numerics in arXiv:2001.00185. In the spherical codes case, the above inequality improves the Kabatyanskii--Levenshtein bound by a factor of $0.2304...$ on geometric average. Along the way, we construct a general class of functions using Stiefel manifolds for which we prove general results and study the improvement factors obtained from them in various settings.and study the improvement factors obtained from them in various settings.