Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Data augmentation (DA) algorithms are widely used for Bayesian inference due to their simplicity. In massive data settings, however, DA algorithms are prohibitively slow because they pass through the full data in any ite…
Bayesian InferenceData AugmentationDistributed ComputingOn the Convergence of Decentralized Adaptive Gradient Methods
Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks. Meanwhile, given the need for distributed computing, distributed…
Distributed ComputingDistributed OptimizationAIMD scheduling and resource allocation in distributed computing systems
We consider the problem of simultaneous scheduling and resource allocation of an incoming flow of requests to a set of computing units. By representing each computing unit as a node, we model the overall system as a mult…
Distributed ComputingSchedulingA Simplicial Model for $KB4_n$: Epistemic Logic with Agents that May Die
The standard semantics of multi-agent epistemic logic S5 is based on Kripke models whose accessibility relations are reflexive, symmetric and transitive. This one dimensional structure contains implicit higher-dimensiona…
Distributed ComputingPrivate Retrieval, Computing and Learning: Recent Progress and Future Challenges
Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: …
Distributed ComputingFederated LearningInformation RetrievalRetrievalDecentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) such as federated learning and swarm lear…
Distributed ComputingEdge-computingFederated LearningPrivacy PreservingHeuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey
Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel mac…
Distributed ComputingSchedulingDistributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochast…
Distributed ComputingStochastic OptimizationStructural biology in the clouds: The WeNMR-EOSC Ecosystem
Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is …
CPUDistributed ComputingScalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the…
Data AugmentationDistributed ComputingModel CompressionDomain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter …
ArticlesDistributed ComputingSelf-Supervised LearningVector Symbolic Architectures as a Computing Framework for Emerging Hardware
This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stoch…
Distributed ComputingPhilosophyMALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated sequence of tasks arising from the coupled…
Atari GamesCPUDistributed ComputingMulti-agent Reinforcement Learning+3Clustered Federated Learning via Generalized Total Variation Minimization
We study optimization methods to train local (or personalized) models for decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of sim…
Distributed ComputingEdge-computingFederated LearningMulti-Task LearningThe Giving Game
This paper describes a basic model of a gift economy in the shape of a Giving Game and reveals the fundamental structure of such a game. Main result is that the game shows a community effect in that a small subgroup of p…
Distributed ComputingMLDS: A Dataset for Weight-Space Analysis of Neural Networks
Neural networks are powerful models that solve a variety of complex real-world problems. However, the stochastic nature of training and large number of parameters in a typical neural model makes them difficult to evaluat…
ClusteringDistributed ComputingMulti-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing
Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However,…
Distributed ComputingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Adaptive rewiring of random neural networks generates convergent-divergent units
Brain networks are adaptively rewired continually, adjusting their topology to bring about functionality and efficiency in sensory, motor and cognitive tasks. In model neural network architectures, adaptive rewiring gene…
Distributed ComputingFoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality
Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads…
Distributed ComputingNeRFNeural RenderingEfficient MPI-based Communication for GPU-Accelerated Dask Applications
Dask is a popular parallel and distributed computing framework, which rivals Apache Spark to enable task-based scalable processing of big data. The Dask Distributed library forms the basis of this computing engine and pr…
BlockingCPUDistributed ComputingGPU