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Distributed Computing

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Showing 101–150 of 379 papers

TitleStatusHype
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications—0
Analog Lagrange Coded Computing—0
Adaptive rewiring of random neural networks generates convergent-divergent units—0
Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-Wise Updates—0
Continuous K-Max Bandits—0
Constrained Multi-Objective Optimization for Automated Machine Learning—0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness—0
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models—0
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer—0
Asynchronous Neural Networks for Learning in Graphs—0
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows—0
DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset—0
DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters—0
A Multi-Batch L-BFGS Method for Machine Learning—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Development details and computational benchmarking of DEPAM—0
Dimensionality Reduction Ensembles—0
Consensus learning: A novel decentralised ensemble learning paradigm—0
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection—0
Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition—0
DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices—0
Asynchronous Announcements—0
Distributed Beamforming Using Decentralized Time Synchronization in a Six-Element Array—0
Conditions for Convergence in Regularized Machine Learning Objectives—0
Computing control invariant sets of nonlinear systems: decomposition and distributed computing—0
A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing—0
Adaptive Explainable Neural Networks (AxNNs)—0
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey—0
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks—0
Computation-and-Communication Efficient Coordinated Multicast Beamforming in Massive MIMO Networks—0
Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks—0
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning—0
Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks—0
Computationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval—0
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing—0
A MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images—0
Compositional and Equilibrium-Free Conditions for Power System Stability -- Part II: Method and Application—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Communication-Efficient Edge AI Inference Over Wireless Networks—0
A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms—0
Akid: A Library for Neural Network Research and Production from a Dataism Approach—0
Active Inference on the Edge: A Design Study—0
A Survey on Distributed Evolutionary Computation—0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning—0
Communication-efficient distributed eigenspace estimation with arbitrary node failures—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
AIMD scheduling and resource allocation in distributed computing systems—0
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss—0
Communication-Efficient Local Decentralized SGD Methods—0
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