| Detecting adversarial samples from artifacts R Feinman, RR Curtin, S Shintre, AB Gardner arXiv preprint arXiv:1703.00410, 2017 | 999 | 2017 |
| 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 | 768* | 2016 |
| Learning Inductive Biases with Simple Neural Networks R Feinman, BM Lake Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018 | 34 | 2018 |
| Learning Task-General Representations with Generative Neuro-Symbolic Modeling R Feinman, BM Lake International Conference on Learning Representations (ICLR), 2021 | 24 | 2021 |
| Generating new concepts with hybrid neuro-symbolic models R Feinman, BM Lake Proceedings of the 42nd Annual Conference of the Cognitive Science Society, 2020 | 12 | 2020 |
| Systems and methods for detecting malware based on event dependencies J Parikh, R Feinman US Patent 10,282,546, 2019 | 9 | 2019 |
| Optimizing a malware detection model using hyperparameters R Feinman, A Parker-Wood, IB Corrales, R Curtin US Patent 10,572,823, 2020 | 8 | 2020 |
| Providing adversarial perturbations to media S Shintre, RA Feinman US Patent 10,542,034, 2020 | 6 | 2020 |
| 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 | 6 | 2019 |
| Systems and methods for detecting malware R Feinman, J Parikh US Patent 10,133,865, 2018 | 6 | 2018 |
| Systems and methods for trichotomous malware classification R Feinman, J Echauz, AB Gardner US Patent 10,366,233, 2019 | 5 | 2019 |
| Compositional diversity in visual concept learning Y Zhou, R Feinman, BM Lake Cognition 244, 105711, 2024 | 4 | 2024 |
| Cascade classifier ordering R Curtin, A Parker-Wood, R Feinman US Patent 10,452,839, 2019 | 2 | 2019 |
| A Linear Systems Theory of Normalizing Flows R Feinman, N Parthasarathy arXiv preprint arXiv:1907.06496, 2019 | 2 | 2019 |
| 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 |
Ryan R. CurtinFree agentAdresse e-mail validée de ratml.org
