ARCHIVEMONTHLY»OCTOBER2019«

OCTOBER2019

READING NOTES

Jeremy M. Cohen, Elan Rosenfeld, J. Zico Kolter. Certified Adversarial Robustness via Randomized Smoothing. ICML 2019.

ADVERSARIAL MACHINE LEARNING DEEP LEARNING

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OCTOBER2019

READING NOTES

Shiyu Liang, Yixuan Li, R. Srikant. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. ICLR 2018.

DEEP LEARNING

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OCTOBER2019

READING NOTES

Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens. Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR abs/1511.06807 (2015).

DEEP LEARNING

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OCTOBER2019

READING NOTES

Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. ICLR 2018.

DEEP LEARNING

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OCTOBER2019

READING NOTES

Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, Cho-Jui Hsieh. The Limitations of Adversarial Training and the Blind-Spot Attack. CoRR abs/1901.04684 (2019).

ADVERSARIAL MACHINE LEARNING DEEP LEARNING

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OCTOBER2019

READING NOTES

Beilun Wang, Ji Gao, Yanjun Qi. A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples. ICLR (Workshop) 2017.

ADVERSARIAL MACHINE LEARNING DEEP LEARNING

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OCTOBER2019

READING NOTES

Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C. Lupu, Fabio Roli. Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization. AISec@CCS 2017.

ADVERSARIAL MACHINE LEARNING DEEP LEARNING

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OCTOBER2019

READING NOTES

Dongyu Meng, Hao Chen. MagNet: A Two-Pronged Defense against Adversarial Examples. ACM Conference on Computer and Communications Security, 2017.

ADVERSARIAL MACHINE LEARNING DEEP LEARNING

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