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Reuben Feinman
Reuben Feinman
Common Sense Machines
Verified email at csm.ai - Homepage
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
10952017
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
827*2016
Learning Inductive Biases with Simple Neural Networks
R Feinman, BM Lake
Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018
372018
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
R Feinman, BM Lake
International Conference on Learning Representations (ICLR), 2021
292021
Generating new concepts with hybrid neuro-symbolic models
R Feinman, BM Lake
Proceedings of the 42nd Annual Conference of the Cognitive Science Society, 2020
122020
Systems and methods for detecting malware based on event dependencies
J Parikh, R Feinman
US Patent 10,282,546, 2019
102019
Optimizing a malware detection model using hyperparameters
R Feinman, A Parker-Wood, IB Corrales, R Curtin
US Patent 10,572,823, 2020
92020
Compositional diversity in visual concept learning
Y Zhou, R Feinman, BM Lake
Cognition 244, 105711, 2024
82024
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
72019
Providing adversarial perturbations to media
S Shintre, RA Feinman
US Patent 10,542,034, 2020
62020
Systems and methods for detecting malware
R Feinman, J Parikh
US Patent 10,133,865, 2018
62018
Systems and methods for trichotomous malware classification
R Feinman, J Echauz, AB Gardner
US Patent 10,366,233, 2019
52019
Cascade classifier ordering
R Curtin, A Parker-Wood, R Feinman
US Patent 10,452,839, 2019
22019
A Linear Systems Theory of Normalizing Flows
R Feinman, N Parthasarathy
arXiv preprint arXiv:1907.06496, 2019
22019
Generative Neuro-Symbolic Models of Concept Learning
R Feinman
New York University, 2023
2023
A Deep Belief Network Approach to Learning Depth from Optical Flow
R Feinman
Brown University, 2015
2015
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