Fetching the paper…
Reading the bibliography…
Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention.
The proof and measurement of association between two things
Charles Spearman. 1904 · 1904
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1988 · 1988
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Machine Learning (1 ed.)
Thomas M. Mitchell. 1997 · 1997
Earlier work this paper cites.
Metamorphic testing: a new approach for generating next test cases
Tsong Y Chen, Shing C Cheung, and Shiu Ming Yiu. 1998 · 1998
Earlier work this paper cites.
Recurrent Neural Networks: Design and Applications (1st ed.)
L. C. Jain and L. R. Medsker. 1999 · 1999
Earlier work this paper cites.
An orchestrated survey of methodologies for automated software test case generation
Saswat Anand, Edmund K Burke, Tsong Yueh Chen, John Clark, Myra B Cohen, Wolfgang Grieskamp, Mark Harman, Mary Jean Harrold, Phil Mcminn, Antonia Bertolino, et al · 2001
Earlier work this paper cites.
Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, Jürgen Schmidhuber, et al · 2001
Earlier work this paper cites.
Search-based software test data generation: a survey
Phil McMinn. 2004 · 2004
Earlier work this paper cites.
Metamorphic testing and its applications. In Proceedings of the 8th International Symposium on Future Software Technology (ISFST 2004) . 346–351
Zhi Quan Zhou, DH Huang, TH Tse, Zongyuan Yang, Haitao Huang, and TY Chen. 2004 · 2004
Earlier work this paper cites.
Greedy layer-wise training of deep networks. In Advances in neural information processing systems . 153–160
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2007 · 2007
Earlier work this paper cites.
Properties of Machine Learning Applications for Use in Metamorphic Testing.. In SEKE , Vol. 8. 867–872
Christian Murphy, Gail E Kaiser, Lifeng Hu, and Leon Wu. 2008 · 2008
Earlier work this paper cites.
A survey of new trends in symbolic execution for software testing and analysis
Corina S Păsăreanu and Willem Visser. 2009 · 2009
Earlier work this paper cites.
Application of metamorphic testing to supervised classifiers. In Quality Software, 2009. QSIC’09. 9th International Conference on . IEEE, 135–144
Xiaoyuan Xie, Joshua Ho, Christian Murphy, Gail Kaiser, Baowen Xu, and Tsong Yueh Chen. 2009 · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) . 807–814
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
An abstraction-refinement approach to verification of artificial neural networks. In Computer Aided Verification . Springer, 243–257
Luca Pulina and Armando Tacchella. 2010 · 2010
Earlier work this paper cites.
Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data
Jan Hauke and Tomasz Kossowski. 2011 · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Add Dramatic Rain to a Photo in Photoshop
2013 · 2013
Earlier work this paper cites.
How to create mist: Photoshop effects for atmospheric landscapes
2013 · 2013
Earlier work this paper cites.
The OpenCV Reference Manual (2.4.9.0 ed.)
2014 · 2014
Earlier work this paper cites.
This Is How Bad Self-Driving Cars Suck In The Rain
2014 · 2014
Earlier work this paper cites.
Practical evasion of a learning-based classifier: A case study. In Security and Privacy (SP), 2014 IEEE Symposium on . IEEE, 197–211
Pavel Laskov et al · 2014
Earlier work this paper cites.
Machine Learning: The High Interest Credit Card of Technical Debt
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young. 2014 · 2014
Earlier work this paper cites.
Intriguing properties of neural networks. In International Conference on Learning Representations (ICLR)
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2014 · 2014
Earlier work this paper cites.
Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers.. In USENIX Security Symposium . 239–254
Gang Wang, Tianyi Wang, Haitao Zheng, and Ben Y Zhao. 2014 · 2014
Earlier work this paper cites.
Affine Transformation
2015a · 2015
Earlier work this paper cites.
Affine Transformations
2015b · 2015
Earlier work this paper cites.
Open Source Computer Vision Library
2015 · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples. In International Conference on Learning Representations (ICLR)
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Towards deep neural network architectures robust to adversarial examples. In International Conference on Learning Representations (ICLR)
Shixiang Gu and Luca Rigazio. 2015 · 2015
Cited alongside, same era.
Using convolutional neural networks for image recognition
Samer Hijazi, Rishi Kumar, and Chris Rowen. 2015 · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 427–436
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2015 · 2015
Cited alongside, same era.
Can we still avoid automatic face detection?. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on . IEEE, 1–9
Michael J Wilber, Vitaly Shmatikov, and Serge Belongie. 2016 · 2016
Later among the works it cites.
Data Mining: Practical machine learning tools and techniques
Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. 2016 · 2016
Later among the works it cites.
Automatically evading classifiers. In Proceedings of the 2016 Network and Distributed Systems Symposium
Weilin Xu, Yanjun Qi, and David Evans. 2016 · 2016
Later among the works it cites.
Improving the robustness of deep neural networks via stability training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 4480–4488
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow. 2016 · 2016
Later among the works it cites.
Autonomous Vehicles Enacted Legislation
2017a · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Uri Shaham, Yutaro Yamada, and Sahand Negahban. 2015 · 2015
Cited alongside, same era.
Chauffeur model
2016a · 2016
Cited alongside, same era.
comma.ai’s steering model
2016 · 2016
Cited alongside, same era.
Epoch model
2016 · 2016
Cited alongside, same era.
Google Auto Waymo Disengagement Report for Autonomous Driving
2016 · 2016
Cited alongside, same era.
Google’s Self-Driving Car Caused Its First Crash
2016 · 2016
Cited alongside, same era.
Rambo model
2016 · 2016
Cited alongside, same era.
Tesla Autopilot
2016 · 2016
Cited alongside, same era.
Baidu Apollo
2017 · 2017
Closest in time.
Inside Waymo’s Secret World for Training Self-Driving Cars
2017 · 2017
Closest in time.
The Numbers Don’t Lie: Self-Driving Cars Are Getting Good
2017b · 2017
Closest in time.
Software 2.0
2017 · 2017
Closest in time.
Tesla’s Self-Driving System Cleared in Deadly Crash
2017 · 2017
Closest in time.
The challenge of verification and testing of machine learning
an Goodfellow and Nicolas Papernot. 2017 · 2017
Closest in time.
Evading Next-Gen AV using A.I
Hyrum Anderson. 2017 · 2017
Closest in time.
Towards evaluating the robustness of neural networks. In Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 39–57
Nicholas Carlini and David Wagner. 2017 · 2017
Closest in time.
Parseval networks: Improving robustness to adversarial examples. In International Conference on Machine Learning . 854–863
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. 2017 · 2017
Closest in time.
Robust Physical-World Attacks on Machine Learning Models
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song. 2017 · 2017
Closest in time.
Detecting Adversarial Samples from Artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. 2017 · 2017
Closest in time.
On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2017a · 2017
Closest in time.
Adversarial Perturbations Against Deep Neural Networks for Malware Classification. In Proceedings of the 2017 European Symposium on Research in Computer Security
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick D. McDaniel. 2017b · 2017
Closest in time.
Safety verification of deep neural networks. In International Conference on Computer Aided Verification . Springer, 3–29
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu. 2017 · 2017
Closest in time.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. 2017 · 2017
Closest in time.
Adversarial examples for generative models
Jernej Kos, Ian Fischer, and Dawn Song. 2017 · 2017
Closest in time.
Delving into Transferable Adversarial Examples and Black-box Attacks. In International Conference on Learning Representations (ICLR)
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Xiaodong Song. 2017 · 2017
Closest in time.
On detecting adversarial perturbations. In International Conference on Learning Representations (ICLR)
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. 2017 · 2017
Closest in time.
Extending Defensive Distillation
Nicolas Papernot and Patrick McDaniel. 2017 · 2017
Closest in time.
Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security . ACM, 506–519
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
Closest in time.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017 · 2017
Closest in time.
Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang. 2017 · 2017
Closest in time.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi. 2017 · 2017
Closest in time.