Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
MLitB: Machine Learning in the Browser
With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the st…
BIG-bench Machine LearningDistributed ComputingGPUPrivacy PreservingBig Learning with Bayesian Methods
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications w…
Bayesian InferenceBIG-bench Machine LearningDistributed ComputingFGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions
Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains. In this paper, we i…
Cloud ComputingDistributed Computinggraph partitioningN$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of the non-negative least-angle regression (…
Distributed Computingfeature selectionregressionImproved Distributed Principal Component Analysis
We study the distributed computing setting in which there are multiple servers, each holding a set of points, who wish to compute functions on the union of their point sets. A key task in this setting is Principal Compon…
ClusteringComputational EfficiencyDistributed ComputingOnline Asynchronous Distributed Regression
Distributed computing offers a high degree of flexibility to accommodate modern learning constraints and the ever increasing size of datasets involved in massive data issues. Drawing inspiration from the theory of distri…
Distributed ComputingregressionUnderstanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
We present a machine learning framework for modeling protein dynamics. Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets generated via molecular dynamics simulations. …
Distributed ComputingDrug DesignModel SelectionChemlambda, universality and self-multiplication
We present chemlambda (or the chemical concrete machine), an artificial chemistry with the following properties: (a) is Turing complete, (b) has a model of decentralized, distributed computing associated to it, (c) works…
Artificial LifeDistributed ComputingA Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms
Computational Grids are a new trend in distributed computing systems. They allow the sharing of geographically distributed resources in an efficient way, extending the boundaries of what we perceive as distributed comput…
CPUDistributed ComputingSchedulingAutomated Reasoning in Modal and Description Logics via SAT Encoding: the Case Study of K(m)/ALC-Satisfiability
In the last two decades, modal and description logics have been applied to numerous areas of computer science, including knowledge representation, formal verification, database theory, distributed computing and, more rec…
Distributed ComputingStructure-Aware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing…
BIG-bench Machine LearningDistributed ComputingEmbed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures. The kernel $k$-means algorithm is however c…
ClusteringDistributed ComputingLinearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Many problems in machine learning and other fields can be (re)for-mulated as linearly constrained separable convex programs. In most of the cases, there are multiple blocks of variables. However, the traditional alternat…
Distributed Computingparameter estimationOn estimating total time to solve SAT in distributed computing environments: Application to the SAT@home project
This paper proposes a method to estimate the total time required to solve SAT in distributed environments via partitioning approach. It is based on the observation that for some simple forms of problem partitioning one c…
CryptanalysisDistributed ComputingConditions for Convergence in Regularized Machine Learning Objectives
Analysis of the convergence rates of modern convex optimization algorithms can be achived through binary means: analysis of emperical convergence, or analysis of theoretical convergence. These two pathways of capturing i…
BIG-bench Machine LearningDistributed ComputingLink Prediction with Social Vector Clocks
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenari…
Distributed ComputingLink PredictionPredictionCellular Tree Classifiers
The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classificatio…
Distributed ComputingGeneral ClassificationDistributed Matrix Completion and Robust Factorization
If learning methods are to scale to the massive sizes of modern datasets, it is essential for the field of machine learning to embrace parallel and distributed computing. Inspired by the recent development of matrix fact…
Collaborative FilteringDistributed ComputingMatrix CompletionComputing High Accuracy Power Spectra with Pico
This paper presents the second release of Pico (Parameters for the Impatient COsmologist). Pico is a general purpose machine learning code which we have applied to computing the CMB power spectra and the WMAP likelihood.…
Distributed ComputingPICOVocal Bursts Intensity Prediction