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Recent advancements in machine learning research, i.e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games.
Publication decisions and their possible effects on inferences drawn from tests of significance—or vice versa
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Harking: Hypothesizing after the results are known
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Publication bias in editorial decision making
Carin M Olson, Drummond Rennie, Deborah Cook, Kay Dickersin, Annette Flanagin, Joseph W Hogan, Qi Zhu, Jennifer Reiling, and Brian Pace · 2002
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Prediction versus accommodation and the risk of overfitting
Christopher Hitchcock and Elliott Sober · 2004
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Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
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Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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David L Donoho, Arian Maleki, Inam Ur Rahman, Morteza Shahram, and Victoria Stodden · 2009
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C Cawley and Nicola LC Talbot · 2010
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On the importance of sharing negative results
Christophe G Giraud-Carrier and Margaret H Dunham · 2010
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Unbiased look at dataset bias
A Torralba and AA Efros · 2011
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Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance
David H Bailey, Jonathan Borwein, Marcos Lopez de Prado, and Qiji Jim Zhu · 2014
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Negativity towards negative results: a discussion of the disconnect between scientific worth and scientific culture, 2014
Natalie Matosin, Elisabeth Frank, Martin Engel, Jeremy S Lum, and Kelly A Newell · 2014
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Publication bias in the social sciences: Unlocking the file drawer
Annie Franco, Neil Malhotra, and Gabor Simonovits · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Fooling ourselves
Regina Nuzzo · 2015
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The extent and consequences of p-hacking in science
Megan L Head, Luke Holman, Rob Lanfear, Andrew T Kahn, and Michael D Jennions · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Deep learning and the information bottleneck principle
N. Tishby and N. Zaslavsky · 2015
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Publication bias and the canonization of false facts
Silas Boye Nissen, Tali Magidson, Kevin Gross, and Carl T Bergstrom · 2016
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The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Fast bayesian optimization of machine learning hyperparameters on large datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 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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Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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1,500 scientists lift the lid on reproducibility
Monya Baker · 2016
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Harking: How badly can cherry-picking and question trolling produce bias in published results?
Kevin R Murphy and Herman Aguinis · 2017
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When does harking hurt? identifying when different types of undisclosed post hoc hypothesizing harm scientific progress
Mark Rubin · 2017
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Through-wall human pose estimation using radio signals
Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, and Dina Katabi · 2018
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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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Ibm’s watson supercomputer recommended ‘unsafe and incorrect’cancer treatments, internal documents show
Casey Ross and Ike Swetlitz · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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Designing explainability of an artificial intelligence system
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Nils Reimers and Iryna Gurevych · 2017
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Results blind science publishing
Joseph J Locascio · 2017
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Can “results blind manuscript evaluation” assuage “publication bias”?
Michael R Hyman · 2017
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On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl Dickstein · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin · 2017
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A genetic programming approach to designing convolutional neural network architectures
Masanori Suganuma, Shinichi Shirakawa, and Tomoharu Nagao · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Taehyun Ha, Sangwon Lee, and Sangyeon Kim · 2018
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Introduction to Machine Learning Interpretability
Patrick Hall and Navdeep Gill · 2018
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Charles H Martin and Michael W Mahoney · 2018
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Explaining deep learning models using causal inference
Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy, and Senthil Mani · 2018
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On reproducible ai: Towards reproducible research, open science, and digital scholarship in ai publications
Odd Erik Gundersen, Yolanda Gil, and David W Aha · 2018
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Artificial intelligence faces reproducibility crisis, 2018
Matthew Hutson · 2018
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Reproducibility vs. replicability: a brief history of a confused terminology
Hans E Plesser · 2018
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Expanding search in the space of empirical ml
Bronwyn Woods · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Towards accountable ai: Hybrid human-machine analyses for characterizing system failure
Besmira Nushi, Ece Kamar, and Eric Horvitz · 2018
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Policy certificates: Towards accountable reinforcement learning
Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill · 2018
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ngraph-he: A graph compiler for deep learning on homomorphically encrypted data
Fabian Boemer, Yixing Lao, and Casimir Wierzynski · 2018
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Building trust in human-centric artificial intelligence
European Commission · 2019
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The prevalence of marginally significant results in psychology over time
Anton Olsson-Collentine, Marcel ALM van Assen, and Chris HJ Hartgerink · 2019
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http://jponttuset.cat/are-gans-the-new-deep/ , 2018
Cvpr statistics · 2019
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https://github.com/lixin4ever/Conference-Acceptance-Rate , 2019
Acceptance rates and submission numbers for main machine learning conferences · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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https://www.scip.ch/en/?labs.20181004 , 2018
Deepfake - an introduction · 2019
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2019
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Ethics guidelines for trustworthy ai
European Commission · 2019
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Comparative accuracy of diagnosis by collective intelligence of multiple physicians vs individual physicians
Michael L Barnett, Dhruv Boddupalli, Shantanu Nundy, and David W Bates · 2019
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High-performance medicine: the convergence of human and artificial intelligence
Eric J Topol · 2019
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Darpa’s explainable artificial intelligence (xai) program
David Gunning · 2019
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https://www.ntsb.gov/investigations/AccidentReports/Reports/HWY18MH010-prelim.pdf , 2018
Preliminary report highway hwy18mh010, by the united states transportation security board · 2019
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
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