Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and…
Distributed ComputingManagementJAM: The JavaScript Agent Machine for Distributed Computing and Simulation with reactive and mobile Multi-agent Systems -- A Technical Report
Agent-based modelling (ABM), simulation (ABS), and distributed computation (ABC) are established methods. The Internet and Web-based technologies are suitable carriers. This paper is a technical report with some tutorial…
Distributed ComputingScalable Differentially Private Clustering via Hierarchically Separated Trees
We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive wi…
ClusteringDimensionality ReductionDistributed ComputingFault-Tolerant Collaborative Inference through the Edge-PRUNE Framework
Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recen…
BIG-bench Machine LearningCollaborative InferenceDistributed ComputingCommunication-efficient distributed eigenspace estimation with arbitrary node failures
We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses. Notably…
Distributed ComputingvalidAsynchronous Neural Networks for Learning in Graphs
This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate the…
Distributed ComputingGraph ClassificationMulti-objective Optimization of Clustering-based Scheduling for Multi-workflow On Clouds Considering Fairness
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Altho…
Cloud ComputingClusteringDistributed ComputingFairness+1MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
While modern internet services, such as chatbots, search engines, and online advertising, demand the use of large-scale deep neural networks (DNNs), distributed training and inference over heterogeneous computing systems…
CPUDistributed ComputingGPUMixture-of-ExpertsDistributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters
With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems. However, the app…
Decision MakingDistributed ComputingDistributed OptimizationProblem DecompositionNebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote cl…
Cross-Lingual Natural Language InferenceDeep LearningDistributed ComputingGPU+3Computing control invariant sets of nonlinear systems: decomposition and distributed computing
In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections withi…
Distributed ComputingADDAI: Anomaly Detection using Distributed AI
When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace a…
Anomaly DetectionDistributed ComputingGeneralized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation
We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset containing multiple inputs, on a distributed computing system with a master node and multiple worker nodes. Generalized Lagr…
Distributed ComputingEdge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing
The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one. State-of-the-art Metaverse architectures rely on a cloud-based appr…
Distributed ComputingEdge-computingNavigateSplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in infer…
3D ClassificationDistributed ComputingNeural Architecture SearchSnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies
Fashion is now among the largest industries worldwide, for it represents human history and helps tell the worlds story. As a result of the Fourth Industrial Revolution, the Internet has become an increasingly important s…
Distributed ComputingGenerative Adversarial NetworkImage RetrievalManagement+1BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deploym…
AutoMLDistributed ComputingQuantizationSlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' hetero…
Distributed ComputingFederated LearningImproving Response Time of Home IoT Services in Federated Learning
For intelligent home IoT services with sensors and machine learning, we need to upload IoT data to the cloud server which cannot share private data for training. A recent machine learning approach, called federated learn…
Distributed ComputingFederated LearningTransfer LearningSky Computing: Accelerating Geo-distributed Computing in Federated Learning
Federated learning is proposed by Google to safeguard data privacy through training models locally on users' devices. However, with deep learning models growing in size to achieve better results, it becomes increasingly …
Distributed ComputingFederated Learning