Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
A Distributed Deep Representation Learning Model for Big Image Data Classification
This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep le…
Distributed ComputingGeneral Classificationimage-classificationImage Classification+1Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge
The 15th Conference on Theoretical Aspects of Rationality and Knowledge (TARK) took place in Carnegie Mellon University, Pittsburgh, USA from June 4 to 6, 2015. The mission of the TARK conferences is to bring together …
Distributed ComputingPhilosophyTowards Anthropo-inspired Computational Systems: the $P^3$ Model
This paper proposes a model which aim is providing a more coherent framework for agents design. We identify three closely related anthropo-centered domains working on separate functional levels. Abstracting from human ph…
Distributed ComputingPhilosophyA Multi-Batch L-BFGS Method for Machine Learning
The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that use a sizeable fraction of the training…
BIG-bench Machine LearningDistributed ComputingLOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing
We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scale search, recommendation, clustering,…
ClusteringDistributed ComputingQuantizationDeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed co…
Distributed ComputingReal-Time Community Detection in Large Social Networks on a Laptop
For a broad range of research, governmental and commercial applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this …
Community DetectionDistributed ComputingNodIO, a JavaScript framework for volunteer-based evolutionary algorithms : first results
JavaScript is an interpreted language mainly known for its inclusion in web browsers, making them a container for rich Internet based applications. This has inspired its use, for a long time, as a tool for evolutionary a…
Distributed ComputingEvolutionary AlgorithmsFast Distributed k-Center Clustering with Outliers on Massive Data
Clustering large data is a fundamental problem with a vast number of applications. Due to the increasing size of data, practitioners interested in clustering have turned to distributed computation methods. In this work…
ClusteringDistributed ComputingStaleness-aware Async-SGD for Distributed Deep Learning
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks. Stochastic Gradient Descent (SGD) is the preferred optimization algorithm for training these networks and as…
Deep LearningDistributed Computingimage-classificationImage ClassificationA Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing
In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociat…
CPUDistributed ComputingSchedulingModel Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study t…
Distributed Computingimage-classificationImage ClassificationTag-Weighted Topic Model For Large-scale Semi-Structured Documents
To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focu…
Distributed ComputingTAGtext-classificationText Classification+1Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node dist…
Collaborative FilteringDistributed ComputingCloudCV: Large Scale Distributed Computer Vision as a Cloud Service
We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastruc…
Distributed ComputingAn Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Mini-batch optimization has proven to be a powerful paradigm for large-scale learning. However, the state of the art parallel mini-batch algorithms assume synchronous operation or cyclic update orders. When worker nodes …
Distributed ComputingStochastic OptimizationGraph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
Distributed computing excels at processing large scale data, but the communication cost for synchronizing the shared parameters may slow down the overall performance. Fortunately, the interactions between parameter and d…
BIG-bench Machine LearningDistributed Computinggraph partitioningUnwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Recent approaches to distributed model fitting rely heavily on consensus ADMM, where each node solves small sub-problems using only local data. We propose iterative methods that solve {\em global} sub-problems over an en…
Distributed ComputingRankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our framework exploits data structure to fact…
Distributed ComputingSchedulingCommunication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distrib…
Binary ClassificationDistributed ComputingDistributed Optimizationregression+1