Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Asynchronous Announcements
We propose a multi-agent epistemic logic of asynchronous announcements, where truthful announcements are publicly sent but individually received by agents, and in the order in which they were sent. Additional to epistemi…
Distributed ComputingTime Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, i.e. …
Distributed ComputingGaussian ProcessesregressionTime Series+2A Security Monitoring Framework For Virtualization Based HEP Infrastructures
High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumpt…
BIG-bench Machine LearningCPUDistributed ComputingParallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors
Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper…
Distributed ComputingBlock CUR: Decomposing Matrices using Groups of Columns
A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the abili…
Distributed ComputingLarge Scale Evolution of Convolutional Neural Networks Using Volunteer Computing
This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part mode…
Distributed ComputingL2 RegularizationEasily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
Many statistical learning problems can be posed as minimization of a sum of two convex functions, one typically a composition of non-smooth and linear functions. Examples include regression under structured sparsity assu…
Distributed ComputingAkid: A Library for Neural Network Research and Production from a Dataism Approach
Neural networks are a revolutionary but immature technique that is fast evolving and heavily relies on data. To benefit from the newest development and newly available data, we want the gap between research and productio…
Distributed ComputingGPUFolksoDrivenCloud: an annotation and process application for social collaborative networking
In this paper we present the FolksoDriven Cloud (FDC) built on Cloud and on Semantic technologies. Cloud computing has emerged in these recent years as the new paradigm for the provision of on-demand distributed computin…
Cloud ComputingDistributed ComputingDistributed Real-Time Sentiment Analysis for Big Data Social Streams
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about what…
Distributed ComputingSentiment AnalysisShort-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
Faced with saturation of Moore's law and increasing size and dimension of data, system designers have increasingly resorted to parallel and distributed computing to reduce computation time of machine-learning algorithms.…
Distributed ComputingFeature-distributed sparse regression: a screen-and-clean approach
Most existing approaches to distributed sparse regression assume the data is partitioned by samples. However, for high-dimensional data (D >> N), it is more natural to partition the data by features. We propose an algori…
Distributed ComputingregressionOnline Learning for Wireless Distributed Computing
There has been a growing interest for Wireless Distributed Computing (WDC), which leverages collaborative computing over multiple wireless devices. WDC enables complex applications that a single device cannot support ind…
Distributed ComputingCoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has…
BIG-bench Machine LearningDistributed ComputingDistributed OptimizationOptimization for Large-Scale Machine Learning with 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 OptimizationComputational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks
We initiate a line of investigation into biological neural networks from an algorithmic perspective. We develop a simplified but biologically plausible model for distributed computation in stochastic spiking neural netwo…
AllDistributed ComputingA Primer on Coordinate Descent Algorithms
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gain…
BIG-bench Machine Learningcompressed sensingDistributed ComputingHorn: A System for Parallel Training and Regularizing of Large-Scale Neural Networks
I introduce a new distributed system for effective training and regularizing of Large-Scale Neural Networks on distributed computing architectures. The experiments demonstrate the effectiveness of flexible model partitio…
Distributed ComputingGeneral ClassificationDistributed Graph Clustering by Load Balancing
Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the past decades, researchers have proposed …
ClusteringDistributed ComputingGraph ClusteringEncoding Cryptographic Functions to SAT Using Transalg System
In this paper we propose the technology for constructing propositional encodings of discrete functions. It is aimed at solving inversion problems of considered functions using state-of-the-art SAT solvers. We implemented…
CryptanalysisDistributed Computing