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ARCHIVEMONTHLY»SEPTEMBER2019«
25
th
SEPTEMBER2019
READING NOTES
Sayantan Sarkar, Ankan Bansal, Upal Mahbub, Rama Chellappa.
UPSET and ANGRI : Breaking High Performance Image Classifiers.
CoRR abs/1707.01159 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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23
th
SEPTEMBER2019
READING NOTES
Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew Botvinick.
On the importance of single directions for generalization
. ICLR, 2018.
DEEP LEARNING
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22
th
SEPTEMBER2019
READING NOTES
Cihang Xie, Zhishuai Zhang, Jianyu Wang, Yuyin Zhou, Zhou Ren, Alan L. Yuille.
Improving Transferability of Adversarial Examples with Input Diversity.
CoRR abs/1803.06978 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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21
th
SEPTEMBER2019
READING NOTES
Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa.
Improving Network Robustness against Adversarial Attacks with Compact Convolution
. CoRR abs/1712.00699 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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20
th
SEPTEMBER2019
READING NOTES
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey E. Hinton.
Regularizing Neural Networks by Penalizing Confident Output Distributions.
ICLR (Workshop), 2017.
DEEP LEARNING
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19
th
SEPTEMBER2019
READING NOTES
Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson.
Adversarial Geometry and Lighting using a Differentiable Renderer.
CoRR abs/1808.02651 (2018).
ADVERSARIAL MACHINE LEARNING
COMPUTER VISION
DEEP LEARNING
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18
th
SEPTEMBER2019
READING NOTES
Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu.
Enhanced Attacks on Defensively Distilled Deep Neural Networks
. CoRR abs/1711.05934 (2017).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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15
th
SEPTEMBER2019
READING NOTES
Yan Zhou, Murat Kantarcioglu, Bowei Xi.
Breaking Transferability of Adversarial Samples with Randomness
. CoRR abs/1805.04613 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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13
th
SEPTEMBER2019
READING NOTES
Xiao Zhang, David Evans.
Cost-Sensitive Robustness against Adversarial Examples
. CoRR abs/1810.09225 (2018).
ADVERSARIAL MACHINE LEARNING
DEEP LEARNING
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11
th
SEPTEMBER2019
READING NOTES
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres.
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
. ICML, 2018.
DEEP LEARNING
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