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Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms.
Strong inference
John R Platt · 1964
Earlier work this paper cites.
Artificial intelligence meets natural stupidity
Drew McDermott · 1976
Earlier work this paper cites.
N-rays: An episode in the history and psychology of science
Mary Jo Nye · 1980
Earlier work this paper cites.
Mathematical writing, 1987
Donald E Knuth, Tracy Larrabee, and Paul M Roberts · 1987
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How evaluation guides ai research: The message still counts more than the medium
Paul R Cohen and Adele E Howe · 1988
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The experimental study of machine learning
Pat Langley and Dennis Kibler · 1991
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A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber · 1991
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Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing
Michael C Mozer · 1994
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Does deep blue use artificial intelligence?
RE Korf · 1997
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Artificiality: The tension between internal and external validity in economic experiments
Arthur Schram · 2005
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Statistics of critical points of gaussian fields on large-dimensional spaces
Alan J Bray and David S Dean · 2007
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The emotion machine: Commonsense thinking, artificial intelligence, and the future of the human mind
Marvin Minsky · 2007
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2008
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Improvements that don’t add up: ad-hoc retrieval results since 1998
Timothy G Armstrong, Alistair Moffat, William Webber, and Justin Zobel · 2009
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Covariate shift by kernel mean matching
Arthur Gretton, Alexander J Smola, Jiayuan Huang, Marcel Schmittfull, Karsten M Borgwardt, and Bernhard Schölkopf · 2009
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What is the best multi-stage architecture for object recognition?
Kevin Jarrett, Koray Kavukcuoglu, Yann LeCun, et al · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Rob Fergus · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Practical recommendations for gradient-based training of deep architectures
Yoshua Bengio · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Counterfactual reasoning and learning systems: The example of computational advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X Charles, D Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson · 2013
Earlier work this paper cites.
Scaling semantic parsers with on-the-fly ontology matching
Tom Kwiatkowski, Eunsol Choi, Yoav Artzi, and Luke Zettlemoyer · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Cited alongside, same era.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
Cited alongside, same era.
Researchers announce advance in image-recognition software, 2014 — Accessed on July 4th, 2018
John Markoff · 2014
Cited alongside, same era.
Combating reinforcement learning’s sisyphean curse with intrinsic fear
Zachary C Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, and Li Deng · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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A multipath network for object detection
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O Pinheiro, Sam Gross, Soumith Chintala, and Piotr Dollár · 2016
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Yoshua Bengio · 2017
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Superintelligence
Nick Bostrom · 2017
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Motion for a european parliament resolution with recommendations to the commission on civil law rules on robotics, 2017
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You don’t have to be Google to build an artificial brain, 2014 — Accessed on July 4th, 2018
Cade Metz · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Cited alongside, same era.
The loss surfaces of multilayer networks
Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun · 2015
Cited alongside, same era.
Estimating the reproducibility of psychological science
Open Science Collaboration et al · 2015
Cited alongside, same era.
Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2015
Cited alongside, same era.
Council of European Union · 2017
Later among the works it cites.
Algorithmic bias in autonomous systems
David Danks and Alex John London · 2017
Later among the works it cites.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
Later among the works it cites.
Caner Hazirbas, Laura Leal-Taixé, and Daniel Cremers · 2017
Later among the works it cites.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2017
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Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao · 2017
Later among the works it cites.
Does mitigating ML’s impact disparity require treatment disparity?
Zachary C Lipton, Alexandra Chouldechova, and Julian McAuley · 2017
Later among the works it cites.
Are GANs created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
Later among the works it cites.
Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy S. Liang · 2017
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Pierre Stock and Moustapha Cisse · 2017
Later among the works it cites.
URL http://coling2018.org/first-call-for-papers/
Coling first call for papers, Accessed on July 4th, 2018 · 2018
Closest in time.
Are all languages equally hard to language-model?
Ryan Cotterell, Sebastian J Mielke, Jason Eisner, and Brian Roark · 2018
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The data that transformed ai research—and possibly the world, 2017 — Accessed on July 4th, 2018
David Gershgorn · 2018
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A modest proposal, Accessed on July 4th, 2018
Zoubin Ghahramani · 2018
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Future publication models at NIPS, Accessed on July 4th, 2018
John Langford · 2018
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Proposal for a new publishing model in computer science, Accessed on July 4th, 2018
Yann LeCun · 2018
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On the state of the art of evaluation in neural language models
Gábor Melis, Chris Dyer, and Phil Blunsom · 2018
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On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2018
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How does batch normalization help optimization? (no, it is not about internal covariate shift)
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
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Winner’s curse? on pace, progress, and empirical rigor
D Sculley, Jasper Snoek, Alex Wiltschko, and Ali Rahimi · 2018
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Neural motifs: Scene graph parsing with global context
Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi · 2018
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