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Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, Halbert White, et al · 1989
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Generalized Additive Models
Trevor Hastie and Robert Tibshirani · 1990
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Generalized additive models for medical research
T Hastie and R Tibshirani · 1995
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Multitask learning
Rich Caruana · 1997
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Sparse spatial autoregressions
R Kelley Pace and Ronald Barry · 1997
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Generalized additive neural networks
William JE Potts · 1999
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Generalized linear and generalized additive models in studies of species distributions: setting the scene
Antoine Guisan, Thomas C Edwards Jr, and Trevor Hastie · 2002
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Convolutional deep belief networks on cifar-10
Alex Krizhevsky · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Multiparameter intelligent monitoring in intensive care ii (mimic-ii): a public-access intensive care unit database
Mohammed Saeed, Mauricio Villarroel, Andrew T Reisner, Gari Clifford, Li-Wei Lehman, George Moody, Thomas Heldt, Tin H Kyaw, Benjamin Moody, and Roger G Mark · 2011
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Accurate intelligible models with pairwise interactions
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Adaptive machine learning for credit card fraud detection
Andrea Dal Pozzolo · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2018
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A Hamprecht, Yoshua Bengio, and Aaron Courville · 2018
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Distill-and-compare: Auditing black-box models using transparent model distillation
Sarah Tan, Rich Caruana, Giles Hooker, and Yin Lou · 2018
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Project explain: Interim report
ICO · 2019
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Interpretml: A unified framework for machine learning interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana · 2019
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Explainable machine learning for scientific insights and discoveries
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Tianqi Chen and Carlos Guestrin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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COMPAS Data and analysis for ‘Machine Bias’
ProPublica · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzkebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D Sculley · 2017
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Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2018
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Ribana Roscher, Bastian Bohn, Marco F Duarte, and Jochen Garcke · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Explainable AI: The Basics - Policy Briefing
The Royal Society · 2019
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The AI revolution in scientific research
The Royal Society and The Alan Turing Institute · 2019
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Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks, 2016
Julia Angwin, Jeff Larson, Lauren Kirchner, and Surya Mattu · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T Barron, and Ren Ng · 2020
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Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality
Christine K Lee, Muntaha Samad, Ira Hofer, Maxime Cannesson, and Pierre Baldi · 2021
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Interpretable machine learning: Fundamental principles and 10 grand challenges
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An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding
Divyanshi Srivastava, Begüm Aydin, Esteban O Mazzoni, and Shaun Mahony · 2021
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Survnam: The machine learning survival model explanation
Lev V Utkin, Egor D Satyukov, and Andrei V Konstantinov · 2021
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