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Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence.
A Step Toward Quantifying Independently Reproducible Machine Learning Research
Edward Raff. 2019 · 1909
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Electronic documents give reproducible research a new meaning. In SEG Technical Program Expanded Abstracts 1992 . Society of Exploration Geophysicists, New Orleans, US, 601–604
Jon F. Claerbout and Martin Karrenbach. 2005 · 1992
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Model-integrated computing
J. Sztipanovits and G. Karsai. 1997 · 1997
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Non-Determinism in TensorFlow ResNets
Miguel Morin and Matthew Willetts. 2020 · 2001
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Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d’Alché Buc, Emily Fox, and Hugo Larochelle. 2020 · 2003
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Storylines of research in diffusion of innovation: a meta-narrative approach to systematic review
Trisha Greenhalgh, Glenn Robert, Fraser Macfarlane, Paul Bate, Olympia Kyriakidou, and Richard Peacock. 2005 · 2004
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Bibliographic coupling and its application to research-front and other core documents
Bo Jarneving. 2007 · 2007
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Concept Drift
Claude Sammut and Michael Harries. 2010 · 2010
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CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Kenneth F. Schulz, Douglas G. Altman, David Moher, and the CONSORT Group. 2010 · 2010
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Anti-Distillation: Improving reproducibility of deep networks
Gil I. Shamir and Lorenzo Coviello. 2020 · 2010
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Reproducible research in computational science
Roger D. Peng. 2011 · 2011
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Trust your science? Open your data and code
Victoria C Stodden. 2011 · 2011
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Putting oncology patients at risk
Bob Carlson. 2012 · 2012
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Augmenting PROV with Plans in P-PLAN: Scientific Processes as Linked Data. In Proceedings of the 2nd International Workshop on Linked Science (CEUR Workshop Proceedings, Vol. 951)
Daniel Garijo and Yolanda Gil. 2012 · 2012
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Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, and Martha White. 2020 · 2012
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The International vocabulary of metrology – Basic and general concepts and associated terms - 3rd edition with minor corrections
Joint Committee for Guides in Metrology. 2012 · 2012
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SPIRIT 2013: new guidance for content of clinical trial protocols
An-Wen Chan, Jennifer M Tetzlaff, Douglas G Altman, Kay Dickersin, and David Moher. 2013 · 2013
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Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. 2013 · 2013
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GPUDet: a deterministic GPU architecture. In Architectural Support for Programming Languages and Operating Systems, ASPLOS 2013, Houston, TX, USA, March 16-20, 2013 , Vivek Sarkar and Rastislav Bodík (Eds.). ACM, 1–12
Hadi Jooybar, Wilson W. L. Fung, Mike O’Connor, Joseph Devietti, and Tor M. Aamodt. 2013 · 2013
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PROV-O: The PROV Ontology
Deborah McGuinness, Timothy Lebo, and Satya Sahoo. 2013 · 2013
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Recomputation. org: Experiences of its first year and lessons learned. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing . IEEE, London, UK, 968–973
Ian P Gent and Lars Kotthoff. 2014 · 2014
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Docker: Lightweight Linux Containers for Consistent Development and Deployment
Dirk Merkel. 2014 · 2014
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Reproducibility in Science Improving the Standard for Basic and Preclinical Research
C. Begley and John Ioannidis. 2015 · 2015
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement
Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman, and Karel GM Moons. 2015 · 2015
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Interoperable Machine Learning Metadata using MEX. In Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015), October 11, 2015 (CEUR Workshop Proceedings, Vol. 1486) , Serena Villata, Jeff Z. Pan, and Mauro Dragoni (Eds.). CEUR-WS.org, Bethlehem, PA, USA
Diego Esteves, Diego Moussallem, Ciro Baron Neto, Jens Lehmann, Maria Cláudia Cavalcanti, and Julio Cesar Duarte. 2015 · 2015
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Estimation of numerical reproducibility on CPU and GPU. In Federated Conference on Computer Science and Information Systems (FedCSIS) . 675–680
Fabienne Jézéquel, Jean-Luc Lamotte, and Issam Saïd. 2015 · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015 · 2015
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Synthesizing information systems knowledge: A typology of literature reviews
Guy Paré, Marie-Claude Trudel, Mirou Jaana, and Spyros Kitsiou. 2015 · 2015
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Data on the Web Best Practices: Data Quality Vocabulary
Riccardo Albertoni and Antoine Isaac. 2016 · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
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What does research reproducibility mean?
Steven N. Goodman, Daniele Fanelli, and John P. A. Ioannidis. 2016 · 2016
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Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, and Michael Berk. 2016 · 2016
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DeepForge: An Open Source, Collaborative Environment for Reproducible Deep Learning. In Reproducibility in Machine Learning Workshop at ICML 2017
Brian Broll and Jimmy Whitaker. 2017 · 2017
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Abstract, link, publish, exploit: An end to end framework for workflow sharing
Daniel Garijo, Yolanda Gil, and Óscar Corcho. 2017 · 2017
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Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup. 2017 · 2017
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Reproducibility in critical care: a mortality prediction case study. In Proceedings of the 2nd Machine Learning for Healthcare Conference (Proceedings of Machine Learning Research, Vol. 68) , Finale Doshi-Velez, Jim Fackler, David Kale, Rajesh Ranganath, Byron Wallace, and Jenna Wiens (Eds.). PMLR, 361–376
Alistair E. W. Johnson, Tom J. Pollard, and Roger G. Mark. 2017 · 2017
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The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research. In IEEE International Conference on Robotics and Automation (ICRA) . 4705–4712
Jürgen Leitner, Adam W. Tow, Niko Sünderhauf, Jake E. Dean, Joseph W. Durham, Matthew Cooper, Markus Eich, Christopher Lehnert, Ruben Mangels, Christopher McCool, Peter T. Kujala, Lachlan Nicholson, Trung Pham, James Sergeant, Liao Wu, Fangyi Zhang, Ben Upcroft, and Peter Corke. 2017 · 2017
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Reproducibility vs. Replicability: A Brief History of a Confused Terminology
Hans E. Plesser. 2018 · 2017
Earlier work this paper cites.
Automatically tracking metadata and provenance of machine learning experiments. In NeurIPS 2017
Sebastian Schelter, Joos-Hendrik Böse, Johannes Kirschnick, Thoralf Klein, and Stephan Seufert. 2017 · 2017
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Terminologies for Reproducible Research
Lorena A. Barba. 2018 · 2018
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KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving
Yukyung Choi, Namil Kim, Soonmin Hwang, Kibaek Park, Jae Shin Yoon, Kyounghwan An, and In So Kweon. 2018 · 2018
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The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy
Cynthia Dwork and Jonathan Ullman. 2018 · 2018
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Reproducible research environments with repo2docker. In In ICML workshop on Reproducible Machine Learning
J. Forde, T. Head, C. Holdgraf, Y. Panda, G. Nalvarete, B. Ragan-Kelley, and E. Sundell. 2018 · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
Earlier work this paper cites.
On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications
Odd Erik Gundersen, Yolanda Gil, and David W. Aha. 2018 · 2018
Earlier work this paper cites.
State of the Art: Reproducibility in Artificial Intelligence. In Proceedings of the the Thirty-Second AAAI Conference on Artificial Intelligence, . AAAI Press, 1644–1651
Odd Erik Gundersen and Sigbjørn Kjensmo. 2018 · 2018
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Reproducible Survival Prediction with SEER Cancer Data. In Proceedings of the 3rd Machine Learning for Healthcare Conference (Proceedings of Machine Learning Research, Vol. 85) , Finale Doshi-Velez, Jim Fackler, Ken Jung, David Kale, Rajesh Ranganath, Byron Wallace, and Jenna Wiens (Eds.). PMLR, Palo Alto, California, 49–66
Stefan Hegselmann, Leonard Gruelich, Julian Varghese, and Martin Dugas. 2018 · 2018
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Deep Reinforcement Learning That Matters. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . AAAI Press, 3207–3214
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. 2018 · 2018
Earlier work this paper cites.
Artificial intelligence faces reproducibility crisis
Matthew Hutson. 2018 · 2018
Earlier work this paper cites.
RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms. In 2nd Reproducibility in Machine Learning Workshop at ICML 2018
Khimya Khetarpal, Zafarali Ahmed, Andre Cianflone, Riashat Islam, and Joelle Pineau. 2018 · 2018
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Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation. In British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018 . BMVA Press, 225
Chengyang Li, Dan Song 0006, Ruofeng Tong, and Min Tang 0001. 2018 · 2018
Cited alongside, same era.
Are GANs Created Equal? A Large-Scale Study. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada . 698–707
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. 2018 · 2018
Cited alongside, same era.
Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail
Marcin Miłkowski, Witold M Hensel, and Mateusz Hohol. 2018 · 2018
Cited alongside, same era.
The Impact of Nondeterminism on Reproducibility in Deep Reinforcement Learning. In 2nd Reproducibility in Machine Learning Workshop at ICML 2018
Prabhat Nagarajan, Garrett Warnell, and Peter Stone. 2018 · 2018
Cited alongside, same era.
Reproducible Containers
Omar S. Navarro Leija, Kelly Shiptoski, Ryan G. Scott, Baojun Wang, Nicholas Renner, Ryan R. Newton, and Joseph Devietti. 2020 · 2020
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Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Beau Norgeot, Giorgio Quer, Brett K. Beaulieu-Jones, Ali Torkamani, Raquel Dias, Milena Gianfrancesco, Rima Arnaout, Isaac S. Kohane, Suchi Saria, Eric Topol, Ziad Obermeyer, Bin Yu, and Atul J Butte. 2020 · 2020
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Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review
Seyedeh Neelufar Payrovnaziri, Zhaoyi Chen, Pablo Rengifo-Moreno, Tim Miller, Jiang Bian, Jonathan H Chen, Xiuwen Liu, and Zhe He. 2020 · 2020
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Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. In 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . Association for Computing Machinery, New York, NY, USA, 771–783
Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, and Nachiappan Nagappan. 2020 · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies
Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, and Hamid Zafar. 2018 · 2018
Cited alongside, same era.
SwarmRob: A Toolkit for Reproducibility and Sharing of Experimental Artifacts in Robotics Research
Aljoscha Pörtner, Martin Hoffmann, and Matthias König. 2018 · 2018
Cited alongside, same era.
A Practical Taxonomy of Reproducibility for Machine Learning Research. In Reproducibility in Machine Learning Workshop at ICML 2018. Stockholm, Sweden
Racheal Tatman, Jake VanderPlas, and Sohier Dane. 2018 · 2018
Cited alongside, same era.
Squib: Reproducibility in Computational Linguistics: Are We Willing to Share?
Martijn Wieling, Josine Rawee, and Gertjan van Noord. 2018 · 2018
Cited alongside, same era.
FactSheets: Increasing trust in AI services through supplier’s declarations of conformity
M. Arnold, R. K. E. Bellamy, M. Hind, S. Houde, S. Mehta, A. Mojsilović, R. Nair, K. Natesan Ramamurthy, A. Olteanu, D. Piorkowski, D. Reimer, J. Richards, J. Tsay, and K. R. Varshney. 2019 · 2019
Cited alongside, same era.
Challenges to the Reproducibility of Machine Learning Models in Health Care
Andrew L. Beam, Arjun K. Manrai, and Marzyeh Ghassemi. 2020 · 2019
Cited alongside, same era.
Unreproducible Research is Reproducible. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 725–734
Xavier Bouthillier, César Laurent, and Pascal Vincent. 2019 · 2019
Cited alongside, same era.
Pragmatic considerations for fostering reproducible research in artificial intelligence
Rickey E. Carter, Zachi I. Attia, Francisco Lopez-Jimenez, and Paul A. Friedman. 2019 · 2019
Cited alongside, same era.
Variability and reproducibility in deep learning for medical image segmentation
Félix Renard, Soulaimane Guedria, Noel De Palma, and Nicolas Vuillerme. 2020 · 2020
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Samantha Cruz Rivera, Xiaoxuan Liu, An-Wen Chan, Alastair K Denniston, Melanie J Calvert, The SPIRIT-AI, and CONSORT-AI Working Group. 2020 · 2020
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Machine learning pipelines: provenance, reproducibility and FAIR data principles
Sheeba Samuel, Frank Löffler, and Birgitta König-Ries. 2020 · 2020
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Recommendations for Reporting Machine Learning Analyses in Clinical Research
Laura M. Stevens, Bobak J. Mortazavi, Rahul C. Deo, Lesley Curtis, and David P. Kao. 2020 · 2020
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Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
Sebastian Vollmer, Bilal A Mateen, Gergo Bohner, Franz J Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S L McAllister, Puja Myles, David Grainger, Mark Birse, Richard Branson, Karel G M Moons, Gary S Collins, John P A Ioannidis, Chris Holmes, and Harry Hemingway. 2020 · 2020
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Going on up to the SPIRIT in AI: will new reporting guidelines for clinical trials of AI interventions improve their rigour?
Paul Wicks, Xiaoxuan Liu, and Alastair K. Denniston. 2020 · 2020
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Introducing the Data Quality Vocabulary (DQV)
Riccardo Albertoni and Antoine Isaac. 2021 · 2021
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Best practices in machine learning for chemistry
Nongnuch Artrith, Keith T. Butler, François Xavier Coudert, Seungwu Han, Olexandr Isayev, Anubhav Jain, and Aron Walsh. 2021 · 2021
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Requirements and reliability of AI in the medical context
Yoganand Balagurunathan, Ross Mitchell, and Issam El Naqa. 2021 · 2021
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Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan, Anirudh Vegesana, Naveen Vivek, Kruthi Krishnappa, Wenxin Jiang, Yung-Hsiang Lu, George K. Thiruvathukal, and James C. Davis. 2021 · 2021
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Rethinking data and metadata in the age of machine intelligence
Martin-Immanuel Bittner. 2021 · 2021
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The IJMEDI checklist for assessment of medical AI
Federico Cabitza and Andrea Campagner. 2021 · 2021
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Trustworthy AI . Lecture Notes in Computer Science, Vol. 12600
Raja Chatila, Virginia Dignum, Michael Fisher, Fosca Giannotti, Katharina Morik, Stuart Russell, and Karen Yeung. 2021 · 2021
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On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward
HeeSun Choi, Cindy Crump, Christian Duriez, Asher Elmquist, Gregory Hager, David Han, Frank Hearl, Jessica Hodgins, Abhinandan Jain, Frederick Leve, Chen Li, Franziska Meier, Dan Negrut, Ludovic Righetti, Alberto Rodriguez, Jie Tan, and Jeff Trinkle. 2021 · 2021
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Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
Gary S Collins, Paula Dhiman, Constanza L Andaur Navarro, Jie Ma, Lotty Hooft, Johannes B Reitsma, Patricia Logullo, Andrew L Beam, Lily Peng, Ben Van Calster, Maarten van Smeden, Richard D Riley, and Karel GM Moons. 2021 · 2021
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Reproducibility standards for machine learning in the life sciences
Benjamin J. Heil, Michael M. Hoffman, Florian Markowetz, Su-In Lee, Casey S. Greene, and Stephanie C. Hicks. 2021 · 2021
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Sim2Real in Robotics and Automation: Applications and Challenges
Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Melissa Mozifian, Florian Golemo, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, and Martha White. 2021 · 2021
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Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Konstantinos Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C. Woodruff, Philippe Lambin, and Luis Martí-Bonmatí. 2021 · 2021
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Reproducibility: Evaluating the Evaluations. In Reproducible Research in Pattern Recognition - Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers (Lecture Notes in Computer Science, Vol. 12636) , Bertrand Kerautret, Miguel Colom, Adrien Krähenbühl, Daniel Lopresti, Pascal Monasse, and Hugues Talbot (Eds.). Springer, 12–23
Daniel Lopresti and George Nagy. 2021 · 2021
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Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State. 2021 · 2021
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Reproducibility in machine learning for health research: Still a ways to go
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, and Marzyeh Ghassemi. 2021 · 2021
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Traceability for Trustworthy AI: A Review of Models and Tools
Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal, and Miguel-Angel Sicilia. 2021 · 2021
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A Taxonomy of Tools for Reproducible Machine Learning Experiments. In Proceedings of the AIxIA 2021 Discussion Papers Workshop (AIxIA DP 2021) , Vol. 3078. CEUR Workshop Proceedings, 65–76
L. Quaranta, F. Calefato, and F. Lanubile. 2021 · 2021
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Research Reproducibility as a Survival Analysis. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . AAAI Press, 469–478
Edward Raff. 2021 · 2021
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From transparency to accountability of intelligent systems: Moving beyond aspirations
Rebecca Williams, Richard Cloete, Jennifer Cobbe, Caitlin Cottrill, Peter Edwards, Milan Markovic, Iman Naja, Frances Ryan, Jatinder Singh, Wei Pang, and et al. 2022 · 2021
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Reproducibility, Transparency and Evaluation of Machine Learning in Health Applications. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, . INSTICC, SciTePress, Vienna, Austria, 685–692
Janusz Wojtusiak. 2021 · 2021
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Reinforcement learning for robot research: A comprehensive review and open issues
Tengteng Zhang and Hongwei Mo. 2021 · 2021
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Reproducibility
Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczyński, and Jerzy Stefanowski. 2022 · 2022
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Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
Elena Bertelli, Laura Mercatelli, Chiara Marzi, Eva Pachetti, Michela Baccini, Andrea Barucci, Sara Colantonio, Luca Gherardini, Lorenzo Lattavo, Maria Antonietta Pascali, Simone Agostini, and Vittorio Miele. 2022 · 2022
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Towards Training Reproducible Deep Learning Models. In Proceedings of the 44th International Conference on Software Engineering . Association for Computing Machinery, New York, NY, USA, 2202–2214
Boyuan Chen, Mingzhi Wen, Yong Shi, Dayi Lin, Gopi Krishnan Rajbahadur, and Zhen Ming (Jack) Jiang. 2022 · 2022
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Data Version Control 2022
2022
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Cardio-metabolic risk modeling and assessment through sensor-based measurements
Daniela Giorgi, Luca Bastiani, Maria Aurora Morales, Maria Antonietta Pascali, Sara Colantonio, and Giuseppe Coppini. 2022 · 2022
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Guides for deep learning submissions: IEEE Transactions on Information Forensics and Security 2022
2022
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Do machine learning platforms provide out-of-the-box reproducibility?
Odd Erik Gundersen, Saeid Shamsaliei, and Richard Juul Isdahl. 2022 · 2022
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Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars
Martin Holen, Kristian Muri Knausgård, and Morten Goodwin. 2022 · 2022
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Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann, Bare Luka Žagar, Nicolás Navarro-Guerrero, and Alois Knoll. 2022 · 2022
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Trustworthy Artificial Intelligence: A Review
Davinder Kaur, Suleyman Uslu, Kaley J. Rittichier, and Arjan Durresi. 2022 · 2022
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Trustworthy AI: From Principles to Practices
Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2022 · 2022
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On the Reproducibility and Replicability of Deep Learning in Software Engineering
Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, and Xiaohu Yang. 2022 · 2022
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The reporting standards of randomised controlled trials in leading medical journals between 2019 and 2020: a systematic review
Mairead McErlean, Jack Samways, Peter J. Godolphin, and Yang Chen. 2022 · 2022
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Replicability, robustness, and reproducibility in psychological science
Brian A Nosek, Tom E Hardwicke, Hannah Moshontz, Aurélien Allard, Katherine S Corker, Anna Dreber, Fiona Fidler, Joe Hilgard, Melissa Kline Struhl, Michèle B Nuijten, et al · 2022
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Adopting the YOLOv4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving
Kamil Roszyk, Michal R. Nowicki, and Piotr Skrzypczynski. 2022 · 2022
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Connected Papers
A. Tarnavsky-Eitan, E. Smolyansky, I Knaan-Harpaz, and S. Perets. 2022 · 2022
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IJCAI - Reproducibility Guidelines
Boris Veytsman. 2022 · 2022
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Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review
Colin George Wyllie Weaver, Robert B Basmadjian, Tyler Williamson, Kerry McBrien, Tolu Sajobi, Devon Boyne, Mohamed Yusuf, and Paul Everett Ronksley. 2022 · 2022
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