Copertina

DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shovan, S M, Khanda, Arindam, Ferdous, S M, Das, Sajal K., Halappanavar, Mahantesh
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Distributed, Parallel, and Cluster Computing Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ESCHER: Efficient and Scalable Hypergraph Evolution Representation with Application to Triad Counting
di: Shovan, S. M., et al.
Pubblicazione: (2025)

A Parallel Algorithm for Finding Robust Spanners in Large Social Networks
di: Khanda, Arindam, et al.
Pubblicazione: (2025)

Anonymized Network Sensing using C++26 std::execution on GPUs
di: Mandulak, Michael, et al.
Pubblicazione: (2025)

GreediRIS: Scalable Influence Maximization using Distributed Streaming Maximum Cover
di: Barik, Reet, et al.
Pubblicazione: (2024)

Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing
di: Ferdous, S M, et al.
Pubblicazione: (2024)

Fused Breadth-First Probabilistic Traversals on Distributed GPU Systems
di: Neff, Reece, et al.
Pubblicazione: (2023)

Parallel Batch-Dynamic Maximal Matching with Constant Work per Update
di: Blelloch, Guy E., et al.
Pubblicazione: (2025)

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning
di: Hamood, Moqbel, et al.
Pubblicazione: (2024)

Parallel Batch Dynamic Vertex Coloring in $O(\log Δ)$ Amortized Update Time
di: Hutton, Chase, et al.
Pubblicazione: (2025)

Parallel $k$-Core Decomposition with Batched Updates and Asynchronous Reads
di: Liu, Quanquan C., et al.
Pubblicazione: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
di: Zhai, Wenbin, et al.
Pubblicazione: (2023)

Herring: Parallel Batch-Order-Fairness on DAG-based Blockchain Consensus
di: Putnik, Marko, et al.
Pubblicazione: (2026)

FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
di: Kahenga, Ferdinand, et al.
Pubblicazione: (2025)

Parallel $k$d-tree with Batch Updates
di: Men, Ziyang, et al.
Pubblicazione: (2024)

Parallel Batch-Dynamic Maximal Independent Set
di: Blelloch, Guy, et al.
Pubblicazione: (2026)

COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training
di: Sakip, Akhmed, et al.
Pubblicazione: (2026)

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation
di: Garner, Kevin, et al.
Pubblicazione: (2026)

GPU-Accelerated Batch-Dynamic Subgraph Matching
di: Qiu, Linshan, et al.
Pubblicazione: (2024)

DeepOps & SLURM: Your GPU Cluster Guide
di: Majee, Arindam
Pubblicazione: (2024)

Energy-Efficient Split Learning for Resource-Constrained Environments: A Smart Farming Solution
di: Soltani, Keiwan, et al.
Pubblicazione: (2025)

ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture
di: Tanwar, Vishesh Kumar, et al.
Pubblicazione: (2025)

Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation
di: Jiang, Xuan, et al.
Pubblicazione: (2024)

Faster Parallel Batch-Dynamic Algorithms for Low Out-Degree Orientation
di: Blelloch, Guy, et al.
Pubblicazione: (2026)

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
di: Wang, Jiaqi, et al.
Pubblicazione: (2024)

HarmonyBatch: Batching multi-SLO DNN Inference with Heterogeneous Serverless Functions
di: Chen, Jiabin, et al.
Pubblicazione: (2024)

Semi-Synchronous Exploration in Dynamic Graphs
di: Saxena, Ashish, et al.
Pubblicazione: (2026)

Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching
di: Pang, Bowen, et al.
Pubblicazione: (2025)

NanoCP: Request-Level Dynamic Context Parallelism for Data-Expert Parallel Decoding
di: Chen, Jiefei, et al.
Pubblicazione: (2026)

A Reinforcement Learning Based Backfilling Strategy for HPC Batch Jobs
di: Kolker-Hicks, Elliot, et al.
Pubblicazione: (2024)

Dynamic Contract Analysis for Parallel Programming Models
di: Oraji, Yussur Mustafa, et al.
Pubblicazione: (2026)

Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
di: Dash, Sajal, et al.
Pubblicazione: (2026)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
di: Yu, Liangkun, et al.
Pubblicazione: (2024)

Parallel Spawning Strategies for Dynamic-Aware MPI Applications
di: Martín-Álvarez, Iker, et al.
Pubblicazione: (2025)

Parallel Order-Based Core Maintenance in Dynamic Graphs
di: Guo, Bin, et al.
Pubblicazione: (2022)

Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
di: Barrak, Amine, et al.
Pubblicazione: (2025)

Performance Comparison of Graph Representations Which Support Dynamic Graph Updates
di: Sahu, Subhajit
Pubblicazione: (2025)

Locality, Not Spectral Mixing, Governs Direct Propagation in Distributed Offline Dynamic Programming
di: Shihab, Ibne Farabi
Pubblicazione: (2026)

Personalized Semi-Supervised Federated Learning for Human Activity Recognition
di: Presotto, Riccardo, et al.
Pubblicazione: (2021)

Parallel Point-to-Point Shortest Paths and Batch Queries
di: Dong, Xiaojun, et al.
Pubblicazione: (2025)

FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
di: Kahenga, Ferdinand, et al.
Pubblicazione: (2025)