메뉴 건너뛰기




Volumn , Issue , 2015, Pages 368-380

Automating model search for large scale machine learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLOUD COMPUTING; CLUSTER COMPUTING; RESOURCE ALLOCATION;

EID: 84958951297     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2806777.2806945     Document Type: Conference Paper
Times cited : (129)

References (48)
  • 2
    • 84870749286 scopus 로고    scopus 로고
    • Apache Mahout. http://mahout.apache.org/.
    • Apache Mahout
  • 3
    • 84958951570 scopus 로고    scopus 로고
    • With Vowpal Wabbit
    • Cluster parallel learning. [With Vowpal Wabbit]. https://github.com/JohnLangford/vowpal-wabbit/wiki/Cluster-parallel.pdf.
    • Cluster Parallel Learning.
  • 5
    • 84969394118 scopus 로고    scopus 로고
    • WEKA. http://www.cs.waikato.ac.nz/ml/weka/.
    • WEKA
  • 8
    • 84911993592 scopus 로고    scopus 로고
    • The stratosphere platform for big data analytics
    • A. Alexandrov et al. The Stratosphere Platform for Big Data Analytics. VLDB, 2014.
    • (2014) VLDB
    • Alexandrov, A.1
  • 11
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Y. Benjamini and Y. Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. JRSS B, 1995.
    • (1995) JRSS B
    • Benjamini, Y.1    Hochberg, Y.2
  • 12
    • 80052915062 scopus 로고    scopus 로고
    • Imagenet large scale visual recognition challenge 2010
    • A. Berg, J. Deng, and F.-F. Li. ImageNet Large Scale Visual Recognition Challenge 2010 (ILSVRC2010), 2010.
    • (2010) ILSVRC2010
    • Berg, A.1    Deng, J.2    Li, F.-F.3
  • 14
    • 84857855190 scopus 로고    scopus 로고
    • Random search for hyper-parameter optimization
    • J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. JMLR, 2012.
    • (2012) JMLR
    • Bergstra, J.1    Bengio, Y.2
  • 15
    • 79957872898 scopus 로고    scopus 로고
    • Hyracks: A flexible and extensible foundation for data-intensive computing
    • V. R. Borkar et al. Hyracks: A Flexible and Extensible Foundation for Data-Intensive Computing. In ICDE, 2011.
    • (2011) ICDE
    • Borkar, V.R.1
  • 17
    • 85013628326 scopus 로고    scopus 로고
    • Big data analytics with small footprint: Squaring the cloud
    • J. Canny and H. Zhao. Big data analytics with small footprint: squaring the cloud. In KDD, 2013.
    • (2013) KDD
    • Canny, J.1    Zhao, H.2
  • 18
    • 84866674680 scopus 로고    scopus 로고
    • Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition
    • J. Deng, J. Krause, A. C. Berg, and L. Fei-Fei. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition. CVPR, 2012.
    • (2012) CVPR
    • Deng, J.1    Krause, J.2    Berg, A.C.3    Fei-Fei, L.4
  • 19
    • 33745295134 scopus 로고    scopus 로고
    • Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
    • E. Even-Dar, S. Mannor, and Y. Mansour. Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems. JMLR, 2006.
    • (2006) JMLR
    • Even-Dar, E.1    Mannor, S.2    Mansour, Y.3
  • 21
    • 79957859069 scopus 로고    scopus 로고
    • Systemml: Declarative machine learning on mapreduce
    • A. Ghoting et al. SystemML: Declarative machine learning on MapReduce. In ICDE, 2011.
    • (2011) ICDE
    • Ghoting, A.1
  • 22
    • 85072980230 scopus 로고    scopus 로고
    • Power-graph: Distributed graph-parallel computation on natural graphs
    • J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. Power-Graph: Distributed Graph-Parallel Computation on Natural Graphs. In OSDI, 2012.
    • (2012) OSDI
    • Gonzalez, J.E.1    Low, Y.2    Gu, H.3    Bickson, D.4    Guestrin, C.5
  • 23
    • 82155174846 scopus 로고    scopus 로고
    • Profiling, what-if analysis, and cost-based optimization of mapreduce programs
    • H. Herodotou and S. Babu. Profiling, what-if analysis, and cost-based optimization of mapreduce programs. VLDB, 2011.
    • (2011) VLDB
    • Herodotou, H.1    Babu, S.2
  • 24
    • 85032751458 scopus 로고    scopus 로고
    • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
    • G. Hinton et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 2012.
    • (2012) Signal Processing Magazine, IEEE
    • Hinton, G.1
  • 27
    • 84969399460 scopus 로고    scopus 로고
    • Non-stochastic best arm identification and hyperparameter optimization
    • K. Jamieson and A. Talwalkar. Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR, 2015.
    • (2015) CoRR
    • Jamieson, K.1    Talwalkar, A.2
  • 28
    • 84908279482 scopus 로고    scopus 로고
    • Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn
    • B. Komer et al. Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn. In ICML workshop on AutoML, 2014.
    • (2014) ICML Workshop on AutoML
    • Komer, B.1
  • 30
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky et al. Imagenet classification with deep convolutional neural networks. NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1
  • 32
    • 84891085706 scopus 로고    scopus 로고
    • Feature selection in enterprise analytics: A demonstration using an r-based data analytics system
    • A. Kumar, P. Konda, and C. Ré. Feature Selection in Enterprise Analytics: A Demonstration using an R-based Data Analytics System. VLDB Demo, 2013.
    • (2013) VLDB Demo
    • Kumar, A.1    Konda, P.2    Ré, C.3
  • 34
    • 0345025793 scopus 로고
    • Stream: Sustainable memory bandwidth in high performance computers
    • University of Virginia
    • J. D. McCalpin. STREAM: Sustainable Memory Bandwidth in High Performance Computers. Technical report, University of Virginia, 1991-2007.
    • (1991) Technical Report
    • McCalpin, J.D.1
  • 35
    • 0038998034 scopus 로고
    • Memory bandwidth and machine balance in current high performance computers
    • J. D. McCalpin. Memory Bandwidth and Machine Balance in Current High Performance Computers. TCCA Newsletter, 1995.
    • (1995) TCCA Newsletter
    • McCalpin, J.D.1
  • 36
    • 84959025988 scopus 로고    scopus 로고
    • Mllib: Machine learning in apache spark
    • X. Meng et al. MLlib: Machine Learning in Apache Spark. CoRR, 2015.
    • (2015) CoRR
    • Meng, X.1
  • 38
    • 77955032649 scopus 로고    scopus 로고
    • Planet: Massively parallel learning of tree ensembles with mapreduce
    • B. Panda, J. S. Herbach, S. Basu, and R. J. Bayardo. Planet: Massively Parallel Learning of Tree Ensembles with MapReduce. VLDB, 2009.
    • (2009) VLDB
    • Panda, B.1    Herbach, J.S.2    Basu, S.3    Bayardo, R.J.4
  • 39
    • 80555140075 scopus 로고    scopus 로고
    • Scikit-learn: Machine learning in python
    • F. Pedregosa et al. Scikit-learn: Machine learning in Python. JMLR, 2011.
    • (2011) JMLR
    • Pedregosa, F.1
  • 40
    • 84968193156 scopus 로고
    • An efficient method for finding the minimum of a function of several variables without calculating derivatives
    • M. J. Powell. An Efficient Method for Finding the Minimum of a Function of Several Variables Without Calculating Derivatives. The computer journal, 1964.
    • (1964) The Computer Journal
    • Powell, M.J.1
  • 41
    • 77953218689 scopus 로고    scopus 로고
    • Random features for large-scale kernel machines
    • A. Rahimi and B. Recht. Random Features for Large-Scale Kernel Machines. In NIPS, 2007.
    • (2007) NIPS
    • Rahimi, A.1    Recht, B.2
  • 44
    • 84894647945 scopus 로고    scopus 로고
    • Mli: An api for distributed machine learning
    • E. R. Sparks, A. Talwalkar, et al. MLI: An API for Distributed Machine Learning. In ICDM, 2013.
    • (2013) ICDM
    • Sparks, E.R.1    Talwalkar, A.2
  • 45
    • 85018371540 scopus 로고    scopus 로고
    • Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
    • C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown. Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. In KDD, 2013.
    • (2013) KDD
    • Thornton, C.1    Hutter, F.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 47
    • 65949107549 scopus 로고    scopus 로고
    • Roofline: An insightful visual performance model for multicore architectures
    • S. Williams, A. Waterman, and D. Patterson. Roofline: An Insightful Visual Performance Model for Multicore Architectures. CACM, 2009.
    • (2009) CACM
    • Williams, S.1    Waterman, A.2    Patterson, D.3
  • 48
    • 85040175609 scopus 로고    scopus 로고
    • Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
    • M. Zaharia et al. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In NSDI, 2012.
    • (2012) NSDI
    • Zaharia, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.