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Distributed Computing

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Showing 251–300 of 379 papers

TitleStatusHype
Secure Resource Allocation via Constrained Deep Reinforcement Learning—0
Securing O-RAN Open Interfaces—0
Sequential Gradient Coding For Straggler Mitigation—0
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products—0
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities—0
Simplicial Models for the Epistemic Logic of Faulty Agents—0
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective—0
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks—0
SnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies—0
Solving Billion-Scale Knapsack Problems—0
Sparse L^1-Autoencoders for Scientific Data Compression—0
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms—0
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems—0
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems—0
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction—0
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization—0
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge—0
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case—0
Structural biology in the clouds: The WeNMR-EOSC Ecosystem—0
Structure-Aware Dynamic Scheduler for Parallel Machine Learning—0
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform—0
Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models—0
Tag-Weighted Topic Model For Large-scale Semi-Structured Documents—0
Taming Momentum in a Distributed Asynchronous Environment—0
The Giving Game—0
The intelligent prediction and assessment of financial information risk in the cloud computing model—0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning—0
Toward Creating Subsurface Camera—0
Towards a Better Theoretical Understanding of Independent Subnetwork Training—0
Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures—0
Towards Anthropo-inspired Computational Systems: the P^3 Model—0
Towards automated neural design: An open source, distributed neural architecture research framework—0
Unconventional application of k-means for distributed approximate similarity search—0
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Unifying Graph Convolutional Networks as Matrix Factorization—0
Universal Swarm Computing by Nanorobots—0
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction—0
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning—0
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware—0
Vehicular Multi-Tier Distributed Computing with Hybrid THz-RF Transmission in Satellite-Terrestrial Integrated Networks—0
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective—0
What graph neural networks cannot learn: depth vs width—0
When Evolutionary Computation Meets Privacy—0
You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models—0
Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications—0
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks—0
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks—0
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning—0
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey—0
Accessibility Barriers in Multi-Terabyte Public Datasets: The Gap Between Promise and Practice—0
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