Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/30.

Adversarial Spheres / The Relationship Between High-Dimensional Geometry and Adversarial Examples (2018) Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow

Of the adversarial examples papers I’ve summarized…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/29.

Towards Deep Learning Models Resistant to Adversarial Attacks (2017) Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

This paper is an attempt to cast previous approaches to the problem of adversarial examples…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/28, with better formatting.

Robust Physical-World Attacks on Deep Learning Models (2017) Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song

This is the second paper…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/27.

Synthesizing Robust Adversarial Examples (2017) Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok

This paper is the one that produced the 3D printed turtle that, when viewed from almost any angle, modern ImageNet-trained image…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/25.

Universal adversarial perturbations (2016) Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard

Adversarial perturbations, to this point, have been image-specific. That is, given an input image one needs to apply a perturbation that would…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/24.

Practical Black-Box Attacks against Machine Learning (2016) Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami

There’s something appealing about seeing someone tear down their own work. Or at least…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/23.

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks (2015) Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami

I read this one so you don’t have to. It’s not that…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/22, with better formatting.

Explaining and Harnessing Adversarial Examples (2015) Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy

By now everyone’s seen the “panda” + “nematode” = “gibbon” photo (below). It’s an unfortunate feature of modern…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/21, with better formatting.

DeViSE: A Deep Visual-Semantic Embedding Model (2013) Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, Tomas Mikolov

When I first heard about this paper in Jeremy…


Part of the series A Month of Machine Learning Paper Summaries. Originally posted here on 2018/11/17, with better formatting.

Skip-Thought Vectors (2015) Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler

This paper is a bit of a step backwards in time compared to…

Mike Plotz

yet another bay area software engineer • learning junkie • searching for the right level of meta • also pie

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