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Rahul Rade
Rahul Rade
EthonAI
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Cited by
Cited by
Year
Helper-based adversarial training: Reducing excessive margin to achieve a better accuracy vs. robustness trade-off
R Rade, SM Moosavi-Dezfooli
ICML 2021 Workshop on Adversarial Machine Learning, 2021
572021
Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
R Rade, SM Moosavi-Dezfooli
International Conference on Learning Representations (ICLR), 2022, 2021
502021
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks
A Hoffmann*, C Fanconi*, R Rade*, J Kohler
ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend …, 2021
472021
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
A Modas*, R Rade*, G Ortiz-Jiménez, SM Moosavi-Dezfooli, P Frossard
European Conference on Computer Vision (ECCV), 2022, 2021
312021
Attacker behaviour profiling using stochastic ensemble of hidden markov models
S Deshmukh, R Rade, DF Kazi
arXiv preprint arXiv:1905.11824, 2019
122019
Tackling toxic online communication with recurrent capsule networks
S Deshmukh, R Rade
2018 Conference on Information and Communication Technology (CICT), 1-7, 2018
102018
Temporal and stochastic modelling of attacker behaviour
R Rade, S Deshmukh, R Nene, AS Wadekar, A Unny
Advances in Data Science: Third International Conference on Intelligent …, 2019
52019
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