Fetching the paper…
Reading the bibliography…
While noise is commonly considered a nuisance in computing systems, a number of studies in neuroscience have shown several benefits of noise in the nervous system from enabling the brain to carry out computations such as probabilistic inference as well as carrying additional information about the stimuli.
Stochastic gradient learning in neural networks
Léon Bottou · 1991
Earlier work this paper cites.
The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
Earlier work this paper cites.
A recurrent network that performs a context-sensitive prediction task
Mark Steijvers and Peter Grünwald · 1996
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
Noise in the nervous system
A Aldo Faisal, Luc PJ Selen, and Daniel M Wolpert · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
The benefits of noise in neural systems: bridging theory and experiment
Mark D McDonnell and Lawrence M Ward · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus
Alessandro Scaglione, Karen A Moxon, Juan Aguilar, and Guglielmo Foffani · 2011
Earlier work this paper cites.
A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, RocíO Alaiz-RodríGuez, Nitesh V Chawla, and Francisco Herrera · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
Earlier work this paper cites.
Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Noise as a resource for computation and learning in networks of spiking neurons
Wolfgang Maass · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning. arxiv, 2015
Y Gal and Z Ghahramani · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. computer vision and pattern recognition (cvpr)
K He, X Zhang, S Ren, and J Sun · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Cited alongside, same era.
Adding gradient noise improves learning for very deep networks
Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens · 2015
Cited alongside, same era.
Dropout distillation
Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 2018
Later among the works it cites.
Dropout distillation for efficiently estimating model confidence
Corina Gurau, Alex Bewley, and Ingmar Posner · 2018
Later among the works it cites.
An alternative view: When does sgd escape local minima?
Robert Kleinberg, Yuanzhi Li, and Yang Yuan · 2018
Later among the works it cites.
A neural network framework for cognitive bias
Johan E Korteling, Anne-Marie Brouwer, and Alexander Toet · 2018
Later among the works it cites.
Certified robustness to adversarial examples with differential privacy
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio · 2017
Cited alongside, same era.
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
Later among the works it cites.
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
Later among the works it cites.
Adnan Siraj Rakin, Zhezhi He, and Deliang Fan · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Later among the works it cites.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Closest in time.
Using videos to evaluate image model robustness
Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le, and Jonathan Shlens · 2019
Closest in time.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Closest in time.
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
Closest in time.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Closest in time.
Interpolated adversarial training: Achieving robust neural networks without sacrificing accuracy
Alex Lamb, Vikas Verma, Juho Kannala, and Yoshua Bengio · 2019
Closest in time.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2019
Closest in time.
Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
Closest in time.
Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, and Jamal Atif · 2019
Closest in time.
Similarity-preserving knowledge distillation
Frederick Tung and Greg Mori · 2019
Closest in time.
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
Closest in time.
Towards understanding the importance of noise in training neural networks
Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, and Tuo Zhao · 2019
Closest in time.
Knowledge distillation beyond model compression
Fahad Sarfraz, Elahe Arani, and Bahram Zonooz · 2020
Closest in time.