Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
With the rapid development of software and distributed computing, Cyber-Physical Systems (CPS) are widely adopted in many application areas, e.g., smart grid, autonomous automobile. It is difficult to detect defects in C…
Deep Reinforcement LearningDistributed Computingreinforcement-learningReinforcement Learning+1Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows
Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although s…
Anomaly DetectionDistributed ComputingBigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms
The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system …
Distributed ComputingFlexible and Scalable Deep Learning with MMLSpark
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark. To achieve this, we have con…
Deep LearningDistributed ComputingFundamental Resource Trade-offs for Encoded Distributed Optimization
Dealing with the shear size and complexity of today's massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern dist…
Distributed ComputingDistributed OptimizationA Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pilla…
BIG-bench Machine LearningDistributed ComputingDistributed OptimizationBlock-Diagonal and LT Codes for Distributed Computing With Straggling Servers
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors. The first scheme is based on partitioning the matrix into submatrices and applying maximum distance separabl…
Distributed ComputingAchieving the time of $1$-NN, but the accuracy of $k$-NN
We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of $k$-NN prediction, while matching (or improving) the faster prediction time of $1$-NN. The approach consists o…
Computational EfficiencyDistributed ComputingPredictionCoded Distributed Computing for Inverse Problems
Computationally intensive distributed and parallel computing is often bottlenecked by a small set of slow workers known as stragglers. In this paper, we utilize the emerging idea of ``coded computation'' to design a nov…
Distributed ComputingModular Resource Centric Learning for Workflow Performance Prediction
Workflows provide an expressive programming model for fine-grained control of large-scale applications in distributed computing environments. Accurate estimates of complex workflow execution metrics on large-scale machin…
BIG-bench Machine LearningDistributed ComputingPredictionSchedulingDSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Machine learning with big data often involves large optimization models. For distributed optimization over a cluster of machines, frequent communication and synchronization of all model parameters (optimization variables…
Distributed ComputingDistributed OptimizationDimensionality Reduction Ensembles
Ensemble learning has had many successes in supervised learning, but it has been rare in unsupervised learning and dimensionality reduction. This study explores dimensionality reduction ensembles, using principal compone…
Dimensionality ReductionDistributed ComputingEnsemble LearningDistributed Very Large Scale Bundle Adjustment by Global Camera Consensus
The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping wi…
Distributed ComputingDistributed OptimizationGIANT: Globally Improved Approximate Newton Method for Distributed Optimization
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally fi…
Distributed ComputingDistributed OptimizationDistributed Deep Neural Networks over the Cloud, the Edge and End Devices
We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN…
Distributed ComputingObject RecognitionSensor FusionArtificial life, complex systems and cloud computing: a short review
Cloud computing is the prevailing mode of designing, creating and deploying complex applications nowadays. Its underlying assumptions include distributed computing, but also new concepts that need to be incorporated in t…
Artificial LifeCloud ComputingDistributed ComputingParallelizing Over Artificial Neural Network Training Runs with Multigrid
Artificial neural networks are a popular and effective machine learning technique. Great progress has been made parallelizing the expensive training phase of an individual network, leading to highly specialized pieces of…
Distributed ComputingGPUA Robust Multi-Batch L-BFGS Method for Machine Learning
This paper describes an implementation of the L-BFGS method designed to deal with two adversarial situations. The first occurs in distributed computing environments where some of the computational nodes devoted to the ev…
BIG-bench Machine LearningBinary ClassificationDistributed ComputingProceedings Sixteenth Conference on Theoretical Aspects of Rationality and Knowledge
This volume consists of papers presented at the Sixteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK) held at the University of Liverpool, UK, from July 24 to 26, 2017. TARK conferences bring …
Distributed ComputingPhilosophyRemote sensing of forests using discrete return airborne LiDAR
Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in n…
Distributed ComputingSegmentation