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OCTOBER2019
READING NOTES
Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Duane S. Boning, Cho-Jui Hsieh.
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
. CoRR abs/1906.06316 (2019).
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OCTOBER2019
READING NOTES
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel.
Efficient Neural Network Robustness Certification with General Activation Functions.
NeurIPS 2018: 4944-4953.
ADVERSARIAL MACHINE LEARNING
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OCTOBER2019
READING NOTES
Vaishnavh Nagarajan, J. Zico Kolter.
Generalization in Deep Networks: The Role of Distance from Initialization
. CoRR abs/1901.01672 (2019).
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OCTOBER2019
READING NOTES
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan.
Likelihood Ratios for Out-of-Distribution Detection.
ICML Workshop, 2019.
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OCTOBER2019
READING NOTES
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy A. Mann, Pushmeet Kohli.
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
. CoRR abs/1810.12715 (2018).
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OCTOBER2019
READING NOTES
Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, Graham W. Taylor.
Batch Normalization is a Cause of Adversarial Vulnerability.
CoRR abs/1905.02161 (2019).
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OCTOBER2019
READING NOTES
David Madras, James Atwood, Alex D'Amour.
Detecting Extrapolation with Influence Functions.
ICML Workshop, 2019.
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OCTOBER2019
READING NOTES
Ch. Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri, Sung-Bae Cho.
Radial basis function neural networks: a topical state-of-the-artsurvey
. Open Computer Science 6(1) (2016).
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OCTOBER2019
READING NOTES
Subutai Ahmad, Luiz Scheinkman.
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations.
CoRR abs/1903.11257 (2019).
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OCTOBER2019
READING NOTES
Pourya Habib Zadeh, Reshad Hosseini, Suvrit Sra.
Deep-RBF Networks Revisited: Robust Classification with Rejection
. CoRR abs/1812.03190 (2018).
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