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Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms


Feb 03, 2021
Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen

* 39 Pages, 40 Figures, Appendix with Supplementary Figures 

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Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models


Jan 06, 2021
Jeroen van Hoof, Joaquin Vanschoren

* ECMLPKDD 2019 Workshop on Automating Data Science 

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Theory-based Habit Modeling for Enhancing Behavior Prediction


Jan 05, 2021
Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniel Lakens, Boris de Ruyter, Wijnand A. IJsselsteijn


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Aerial Imagery Pixel-level Segmentation


Dec 03, 2020
Michael R. Heffels, Joaquin Vanschoren

* 30 pages, 15 figures, 4 tables. Code available through GitHub repo at https://github.com/mrheffels/aerial-imagery-segmentation 

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Importance of Tuning Hyperparameters of Machine Learning Algorithms


Jul 15, 2020
Hilde J. P. Weerts, Andreas C. Mueller, Joaquin Vanschoren


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GAMA: a General Automated Machine learning Assistant


Jul 09, 2020
Pieter Gijsbers, Joaquin Vanschoren


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Adaptation Strategies for Automated Machine Learning on Evolving Data


Jun 09, 2020
Bilge Celik, Joaquin Vanschoren

* 12 pages, 8 figures (15 counting subfigures), submitted to TPAMI - AutoML Special Issue 

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OpenML-Python: an extensible Python API for OpenML


Nov 06, 2019
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter


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An Open Source AutoML Benchmark


Jul 01, 2019
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren

* Accepted paper at the AutoML Workshop at ICML 2019. Code: https://github.com/openml/automlbenchmark/ Accompanying website: https://openml.github.io/automlbenchmark/ 

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A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers


Jun 11, 2019
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho

* Information Sciences, Volume 501, 2019. Pages 193-221, ISSN 0020-0255 
* 49 pages, 11 figures 

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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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An empirical study on hyperparameter tuning of decision trees


Dec 05, 2018
Rafael Gomes Mantovani, Tomáš Horváth, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho

* 36 pages, 14 figures 

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Transformative Machine Learning


Nov 08, 2018
Ivan Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren, Ross D. King


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Meta-Learning: A Survey


Oct 08, 2018
Joaquin Vanschoren


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Towards Reproducible Empirical Research in Meta-Learning


Aug 30, 2018
Adriano Rivolli, Luís P. F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho


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ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies


Jul 14, 2018
Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar

* Poster, selected for the 2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm, Sweden 

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Layered TPOT: Speeding up Tree-based Pipeline Optimization


Mar 12, 2018
Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson

* Update to include a reference to Zutty et al. after it was brought to our attention 

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Meta-QSAR: a large-scale application of meta-learning to drug design and discovery


Sep 12, 2017
Ivan Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa Soldatova, Ross D. King

* 33 pages and 15 figures. Manuscript accepted for publication in Machine Learning Journal. This is the author's pre-print version 

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OpenML Benchmarking Suites and the OpenML100


Aug 11, 2017
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, Joaquin Vanschoren

* Submitted to MLOSS 

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OpenML: An R Package to Connect to the Machine Learning Platform OpenML


May 04, 2017
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl


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ASlib: A Benchmark Library for Algorithm Selection


Apr 06, 2016
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Frechette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren

* Accepted to be published in Artificial Intelligence Journal 

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OpenML: networked science in machine learning


Aug 01, 2014
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luis Torgo

* SIGKDD Explor. Newsl. 15, 2 (June 2014), 49-60 
* 12 pages, 10 figures 

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Open science in machine learning


Feb 24, 2014
Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong


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