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

Hugging Face - Blog
Hugging Face - Blog
Stack Overflow Blog
Stack Overflow Blog
量子位
腾讯CDC
N
Netflix TechBlog - Medium
aimingoo的专栏
aimingoo的专栏
小众软件
小众软件
S
SegmentFault 最新的问题
A
About on SuperTechFans
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
G
Google Developers Blog
U
Unit 42
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
雷峰网
雷峰网
罗磊的独立博客
Vercel News
Vercel News
L
LangChain Blog
V
V2EX
P
Proofpoint News Feed
M
MIT News - Artificial intelligence
博客园 - Franky
V
Visual Studio Blog
J
Java Code Geeks

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Proceedings International Workshop on Formal Engineering ...
2017-03-20 · via cs.DC updates on arXiv.org

These are the proceedings of the 14th International Workshop on Formal Engineering approaches to Software Components and Architectures (FESCA). The workshop was held on April 22, 2017 in Uppsala (Sweden) as a satellite event to the European Joint Conference on Theory and Practice of Software (ETAPS'17). The aim of the FESCA workshop is to bring together junior researchers from formal methods, software engineering, and industry interested in the development and application of formal modelling approaches as well as associated analysis and reasoning techniques with practical benefits for software engineering. In recent years, the growing importance of functional correctness and the increased relevance of system quality properties (e.g. performance, reliability, security) have stimulated the emergence of analytical and modelling techniques for the design and development of software systems. With the increasing complexity and utilization of today's software systems, FESCA aims at addressing two research questions: (1) what role is played by the software design phase in the systematic addressing of the analytical and modelling challenges, and (2) how can formal and semi-formal techniques be effectively applied to make the issues easier to address automatically, with lower human intervention.