Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems
Trade-offs between accuracy and efficiency pervade law, public health, and other non-computing domains, which have developed policies to guide how to balance the two in conditions of uncertainty. While computer science a…
Autonomous VehiclesDistributed ComputingSTORM: Foundations of End-to-End Empirical Risk Minimization on the Edge
Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression and classification models. To analyze m…
Distributed ComputingregressionExact Support Recovery in Federated Regression with One-shot Communication
Federated learning provides a framework to address the challenges of distributed computing, data ownership and privacy over a large number of distributed clients with low computational and communication capabilities. In …
Distributed ComputingFederated LearningModel PoisoningregressionArtificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet o…
Distributed ComputingGPUManagementMANGO: A Python Library for Parallel Hyperparameter Tuning
Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian opti…
Bayesian OptimizationBIG-bench Machine LearningDistributed ComputingDistributed Optimization+2Distributed Dynamic Economic Dispatch using Alternating Direction Method of Multipliers
With the proliferation of distributed energy resources and the volume of data stored due to advancement in metering infrastructure, energy management in power system operation needs distributed computing. In this paper, …
Distributed Computingenergy managementManagementLearning, transferring, and recommending performance knowledge with Monte Carlo tree search and neural networks
Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single indiv…
Distributed ComputingIt is Time for New Perspectives on How to Fight Bloat in GP
The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches dominate the landscape, available multi-core,…
Distributed ComputingEvolutionary AlgorithmsCommunication-Efficient Edge AI Inference Over Wireless Networks
Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wir…
Distributed ComputingEdge-computingIntelligent CommunicationBrain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that …
Distributed ComputingMultimodal AssociationAdaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results. We develop a new framework called Ad…
Distributed ComputingMulti-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing
To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with…
Cloud ComputingDistributed ComputingEdge-computingMulti-agent Reinforcement Learning+4Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing
Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edg…
CPUDistributed ComputingDiversityFiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibil…
Distributed ComputingReinforcement LearningReinforcement Learning (RL)Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization. Given that exact coordinate minimization is impossible due to the unknown stochastic nature of the objective fu…
Distributed ComputingAutomata for Hyperlanguages
Hyperproperties lift conventional trace properties from a set of execution traces to a set of sets of execution traces. Hyperproperties have been shown to be a powerful formalism for expressing and reasoning about inform…
Distributed ComputingCommunication-Efficient Distributed SVD via Local Power Iterations
We study distributed computing of the truncated singular value decomposition problem. We develop an algorithm that we call \texttt{LocalPower} for improving communication efficiency. Specifically, we uniformly partition …
Distributed ComputingHoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforce…
Distributed Computingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Matrix decomposition is one of the fundamental tools to discover knowledge from big data generated by modern applications. However, it is still inefficient or infeasible to process very big data using such a method in a …
ClusteringDistributed ComputingBLCS: Brain-Like based Distributed Control Security in Cyber Physical Systems
Cyber-physical system (CPS) has operated, controlled and coordinated the physical systems integrated by a computing and communication core applied in industry 4.0. To accommodate CPS services, fog radio and optical netwo…
BlockingDistributed ComputingRelation Network