'Adversarial examples' diaporamas de présentation

Regularization for Deep Learning

Regularization for Deep Learning. C. Lee Giles. Thanks to Alexander Ororbia and Sargur Srihari. What is Regularization?. Goals of Regularization. Regularize Your Estimator!. regularizer. Regularization & Model Types. The Value of Regularization. Model family. Finding the Best Model.

1.9k views • 121 slides

Fooling ConvNets and Adversarial Examples

Fooling ConvNets and Adversarial Examples. Presented by: Aroma Mahendru. ECE 6504 Deep Learning for Perception. Presentation Overview. I ntriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus

426 views • 42 slides

Safe and Robust Deep Learning

300 views • 28 slides

Adversarial Defense By Convolutional Sparse Coding

255 views • 22 slides

Opening Remarks of Research Forum Deep Learning and Security Workshop 2017

Opening Remarks of Research Forum Deep Learning and Security Workshop 2017. Chang Liu UC Berkeley. Deep Learning and Security is a trending topic in academia in 2017. Best Papers in Security Conferences Towards Evaluating the Robustness of Neural Networks (Oakland 2017 Best Student Paper)

195 views • 19 slides