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In order to fully harness the potential of machine learning, it is crucial to establish a system that renders the field more accessible and less daunting for individuals who may not possess a comprehensive understanding of its intricacies.
A brief survey of web data extraction tools
Alberto HF Laender, Berthier A Ribeiro-Neto, Altigran S Da Silva, and Juliana S Teixeira · 2002
Earlier work this paper cites.
The WEKA data mining software: an update
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten · 2009
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Human-centred machine learning
Marco Gillies, Rebecca Fiebrink, Atau Tanaka, Jérémie Garcia, Frédéric Bevilacqua, Alexis Heloir, Fabrizio Nunnari, Wendy Mackay, Saleema Amershi, Bongshin Lee, and others · 2016
Earlier work this paper cites.
Mllib: Machine learning in apache spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, and others · 2016
Earlier work this paper cites.
TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal S Olson and Jason H Moore · 2016
Earlier work this paper cites.
"Why should i trust you?" Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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The synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
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UX design innovation: Challenges for working with machine learning as a design material
Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman · 2017
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Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management
Florian Kache and Stefan Seuring · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Sensegen: A deep learning architecture for synthetic sensor data generation
Moustafa Alzantot, Supriyo Chakraborty, and Mani Srivastava · 2017
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
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Data scarcity, robustness and extreme multi-label classification
Rohit Babbar and Bernhard Schölkopf · 2019
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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SynSys: A synthetic data generation system for healthcare applications
Jessamyn Dahmen and Diane Cook · 2019
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, and others · 2020
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Investigating how experienced UX designers effectively work with machine learning
Qian Yang, Alex Scuito, John Zimmerman, Jodi Forlizzi, and Aaron Steinfeld · 2018
Cited alongside, same era.
AutoML: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2021
Later among the works it cites.