Reuben Feinman
Title
Cited by
Cited by
Year
Detecting adversarial samples from artifacts
R Feinman, RR Curtin, S Shintre, AB Gardner
arXiv preprint arXiv:1703.00410, 2017
3632017
cleverhans v2. 0.0: an adversarial machine learning library
N Papernot, I Goodfellow, R Sheatsley, R Feinman, P McDaniel
arXiv preprint arXiv:1610.00768 10, 2016
357*2016
Learning Inductive Biases with Simple Neural Networks
R Feinman, BM Lake
Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018
112018
Systems and methods for detecting malware
R Feinman, J Parikh
US Patent 10,133,865, 2018
22018
Generating new concepts with hybrid neuro-symbolic models
R Feinman, BM Lake
Proceedings of the 42nd Annual Conference of the Cognitive Science Society, 2020
12020
Systems and methods for trichotomous malware classification
R Feinman, J Echauz, AB Gardner
US Patent 10,366,233, 2019
12019
Systems and methods for detecting malware based on event dependencies
J Parikh, R Feinman
US Patent 10,282,546, 2019
12019
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
R Feinman, BM Lake
arXiv preprint arXiv:2006.14448, 2020
2020
Optimizing a malware detection model using hyperparameters
R Feinman, A Parker-Wood, IB Corrales, R Curtin
US Patent 10,572,823, 2020
2020
Providing adversarial perturbations to media
S Shintre, RA Feinman
US Patent 10,542,034, 2020
2020
Cascade classifier ordering
R Curtin, A Parker-Wood, R Feinman
US Patent 10,452,839, 2019
2019
A Linear Systems Theory of Normalizing Flows
R Feinman, N Parthasarathy
arXiv preprint arXiv:1907.06496, 2019
2019
Learning a smooth kernel regularizer for convolutional neural networks
R Feinman, BM Lake
Proceedings of the 41st Annual Conference of the Cognitive Science Society, 2019
2019
A Deep Belief Network Approach to Learning Depth from Optical Flow
R Feinman
Brown University, 2015
2015
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Articles 1–14