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Machine learning models are notoriously difficult to interpret and debug.
What every computer scientist should know about floating-point arithmetic
David Goldberg · 1991
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A practical tutorial on modified condition/decision coverage
Kelly J Hayhurst, Dan S Veerhusen, John J Chilenski, and Leanna K Rierson · 2001
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Cute: a concolic unit testing engine for c
Koushik Sen, Darko Marinov, and Gul Agha · 2005
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Distance-sensitive bloom filters
Adam Kirsch and Michael Mitzenmacher · 2006
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Neural networks in organizational research: Applying pattern recognition to the analysis of organizational behavior
David Scarborough and Mark John Somers · 2006
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American fuzzy lop, 2007
Michal Zalewski · 2007
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Real time operation of smart grids via FCN networks and optimal power flow
Pierluigi Siano, Carlo Cecati, Hao Yu, and Janusz Kolbusz · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Scalable nearest neighbor algorithms for high dimensional data
Marius Muja and David G Lowe · 2014
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An empirical evaluation of deep learning on highway driving
Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, et al · 2015
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The unreasonable effectiveness of recurrent neural networks
Andrej Karpathy · 2015
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Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2015
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Announcing oss-fuzz: Continuous fuzzing for open source software
Mike Aizatsky, Kostya Serebryany, Oliver Chang, Abhishek Arya, and Meredith Whittaker · 2016
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016
In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Reproducibility in Machine Learning Research , 2017
Rosemary Nan Ke, Anirudh Goyal, Alex Lamb, Joelle Pineau, Samy Bengio, and Yoshua Bengio, editors · 2017
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Are GANs created equal? A large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
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Deepxplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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The mythos of model interpretability
Zachary C Lipton · 2016
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Augustus Odena · 2016
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Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
J. W Rae, J. J Hunt, T. Harley, I. Danihelka, A. Senior, G. Wayne, A. Graves, and T. P Lillicrap · 2016
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Libfuzzer: A library for coverage-guided fuzz testing (within llvm), 2016
K Serebryany · 2016
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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 · 2017
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Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2017
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Feature-guided black-box safety testing of deep neural networks
Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska · 2017
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, and Ian Goodfellow · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Lei Ma, Felix Juefei-Xu, Jiyuan Sun, Chunyang Chen, Ting Su, Fuyuan Zhang, Minhui Xue, Bo Li, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang · 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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Is generator conditioning causally related to gan performance?
Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, and Ian J. Goodfellow · 2018
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