Simultaneous Perturbation Stochastic Approximation for Few-Shot Learning
Authors: Andrei Boiarov, Oleg Granichin, Olga Granichina | Published: 2020-06-09
学習の改善
Stealing Deep Reinforcement Learning Models for Fun and Profit
Authors: Kangjie Chen, Shangwei Guo, Tianwei Zhang, Xiaofei Xie, Yang Liu | Published: 2020-06-09 | Updated: 2020-12-22
A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks
Authors: Maria Grammatopoulou, Aris Kanellopoulos, Kyriakos G. ~Vamvoudakis, Nathan Lau | Published: 2020-06-06
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Authors: Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu, Ambra Demontis, Battista Biggio, Marco Gori, Fabio Roli | Published: 2020-06-06 | Updated: 2021-12-29
Towards Understanding Fast Adversarial Training
Authors: Bai Li, Shiqi Wang, Suman Jana, Lawrence Carin | Published: 2020-06-04
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization
Authors: Mayumi Ohta, Nathaniel Berger, Artem Sokolov, Stefan Riezler | Published: 2020-06-02 | Updated: 2020-06-29
Online Robustness Training for Deep Reinforcement Learning
Authors: Marc Fischer, Matthew Mirman, Steven Stalder, Martin Vechev | Published: 2019-11-03 | Updated: 2019-11-22
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Authors: Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein | Published: 2019-10-25
A Note on Our Submission to Track 4 of iDASH 2019
Authors: Marcel Keller, Ke Sun | Published: 2019-10-24
Adversarial Robustness Against the Union of Multiple Perturbation Models
Authors: Pratyush Maini, Eric Wong, J. Zico Kolter | Published: 2019-09-09 | Updated: 2020-07-28