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Several works have proposed Simplicity Bias (SB)---the tendency of standard training procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why neural networks generalize well [Arpit et al.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
Peter L Bartlett · 1998
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Improving neural networks with dropout
Nitish Srivastava · 2013
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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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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Nicolas Papernot, Patrick D McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Towards evaluating the robustness of neural networks. arxiv e-prints, page
Nicholas Carlini and David Wagner · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Densely connected convolutional networks. corr abs/1608.06993 (2016)
Gao Huang, Zhuang Liu, and Kilian Q Weinberger · 2016
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles. arxiv e-prints, page
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Earlier work this paper cites.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
Earlier work this paper cites.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Earlier work this paper cites.
Sgd learns over-parameterized networks that provably generalize on linearly separable data
Alon Brutzkus, Amir Globerson, Eran Malach, and Shai Shalev-Shwartz · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Boosting adversarial attacks with momentum. arxiv preprint
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Xiaolin Hu, J Li, and J Zhu · 2017
Earlier work this paper cites.
Gintare Karolina Dziugaite and Daniel M Roy · 2017
Earlier work this paper cites.
Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
Earlier work this paper cites.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
The implicit bias of gradient descent on nonseparable data
Ziwei Ji and Matus Telgarsky · 2019
Later among the works it cites.
Improving adversarial robustness of ensembles with diversity training
Sanjay Kariyappa and Moinuddin K Qureshi · 2019
Later among the works it cites.
Do deep neural networks learn shallow learnable examples first?
Karttikeya Mangalam and Vinay Uday Prabhu · 2019
Later among the works it cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
Later among the works it cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
Later among the works it cites.
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro · 2017
Cited alongside, same era.
Failures of gradient-based deep learning
Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah · 2017
Cited alongside, same era.
Ensemble methods as a defense to adversarial perturbations against deep neural networks
Thilo Strauss, Markus Hanselmann, Andrej Junginger, and Holger Ulmer · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
Cited alongside, same era.
Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C Zhang, S Bengio, M Hardt, B Recht, and O Vinyals · 2017
Cited alongside, same era.
Vaishnavh Nagarajan and J Zico Kolter · 2019
Later among the works it cites.
Sgd on neural networks learns functions of increasing complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L Edelman, Fred Zhang, and Boaz Barak · 2019
Later among the works it cites.
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré · 2019
Later among the works it cites.
Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
Later among the works it cites.
Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
Later among the works it cites.
The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
Later among the works it cites.
Gradmask: reduce overfitting by regularizing saliency
Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2019
Later among the works it cites.
Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Perez, Chico Q. Camargo, and Ard A. Louis · 2019
Later among the works it cites.
High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
Later among the works it cites.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Later among the works it cites.
Learning robust representations by projecting superficial statistics out
Haohan Wang, Zexue He, and Eric P. Xing · 2019
Later among the works it cites.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
Later among the works it cites.
Intriguing properties of adversarial training at scale
Cihang Xie and Alan Yuille · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
Later among the works it cites.
Unrestricted adversarial examples via semantic manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David Forsyth · 2020
Closest in time.
Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss
Lenaic Chizat and Francis Bach · 2020
Closest in time.
Evaluating nlp models via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, et al · 2020
Closest in time.
What shapes feature representations? exploring datasets, architectures, and training
Katherine L Hermann and Andrew K Lampinen · 2020
Closest in time.
Hold me tight! influence of discriminative features on deep network boundaries
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2020
Closest in time.
Improving adversarial robustness through progressive hardening
Chawin Sitawarin, Supriyo Chakraborty, and David Wagner · 2020
Closest in time.
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
Closest in time.
Kernel and rich regimes in overparametrized models
Blake Woodworth, Suriya Gunasekar, Jason D Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro · 2020
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