The Sacred Infrastructure for Computational Research
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[1] Ralph Johnson,et al. Design patterns: elements of reuseable object-oriented software , 1994 .
[2] Computing In Science & Engineering: Web Computing - Java and Grande Applications , 2003, IEEE Distributed Syst. Online.
[3] Bertram Ludäscher,et al. Kepler: an extensible system for design and execution of scientific workflows , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..
[4] Cláudio T. Silva,et al. VisTrails: enabling interactive multiple-view visualizations , 2005, VIS 05. IEEE Visualization, 2005..
[5] Daniel J. Blankenberg,et al. Galaxy: a platform for interactive large-scale genome analysis. , 2005, Genome research.
[6] Geoff Holmes,et al. Experiment databases , 2012, Machine Learning.
[7] Kevin Leyton-Brown,et al. Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.
[8] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[9] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[10] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[11] Philip J. Guo. CDE: A Tool for Creating Portable Experimental Software Packages , 2012, Computing in Science & Engineering.
[12] Bill Howe. CDE: A Tool for Creating Portable Experimental Software Packages , 2012 .
[13] Andrew P. Davison. Automated Capture of Experiment Context for Easier Reproducibility in Computational Research , 2012, Computing in Science & Engineering.
[14] Andreas Wombacher,et al. ProvenanceCurious: a tool to infer data provenance from scripts , 2013, EDBT '13.
[15] Katharina Eggensperger,et al. Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters , 2013 .
[16] Ian T. Foster,et al. Using Provenance for Repeatability , 2013, TaPP.
[17] Carole A. Goble,et al. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud , 2013, Nucleic Acids Res..
[18] Leslie Greengard,et al. Fast Direct Methods for Gaussian Processes and the Analysis of NASA Kepler Mission Data , 2014 .
[19] Luís Torgo,et al. OpenML: networked science in machine learning , 2014, SKDD.
[20] Tony R. Martinez,et al. An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage , 2014, MetaSel@ECAI.
[21] Kevin Leyton-Brown,et al. An Efficient Approach for Assessing Hyperparameter Importance , 2014, ICML.
[22] Yves Janin,et al. CARE, the comprehensive archiver for reproducible execution , 2014, TRUST '14.
[23] Juliana Freire,et al. Collecting and Analyzing Provenance on Interactive Notebooks: When IPython Meets noWorkflow , 2015, TaPP.
[24] Prabhat,et al. Scalable Bayesian Optimization Using Deep Neural Networks , 2015, ICML.
[25] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[26] Aaron Klein,et al. Bayesian Optimization with Robust Bayesian Neural Networks , 2016, NIPS.
[27] Dennis Shasha,et al. ReproZip: Computational Reproducibility With Ease , 2016, SIGMOD Conference.
[28] Jürgen Schmidhuber,et al. LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[29] Vasan Subramanian,et al. MongoDB , 2019, Pro MERN Stack.