| Dynamic routing between capsules S Sabour, N Frosst, GE Hinton Advances in neural information processing systems 30, 2017 | 6261 | 2017 |
| Matrix capsules with EM routing GE Hinton, S Sabour, N Frosst International conference on learning representations, 2018 | 1350 | 2018 |
| Distilling a neural network into a soft decision tree N Frosst, G Hinton arXiv preprint arXiv:1711.09784, 2017 | 790 | 2017 |
| Neural additive models: Interpretable machine learning with neural nets R Agarwal, L Melnick, N Frosst, X Zhang, B Lengerich, R Caruana, ... Advances in neural information processing systems 34, 4699-4711, 2021 | 501 | 2021 |
| Analyzing and improving representations with the soft nearest neighbor loss N Frosst, N Papernot, G Hinton International conference on machine learning, 2012-2020, 2019 | 179 | 2019 |
| On computational modeling of visual saliency: Examining what’s right, and what’s left NDB Bruce, C Wloka, N Frosst, S Rahman, JK Tsotsos Vision research 116, 95-112, 2015 | 101 | 2015 |
| Detecting and diagnosing adversarial images with class-conditional capsule reconstructions Y Qin, N Frosst, S Sabour, C Raffel, G Cottrell, G Hinton arXiv preprint arXiv:1907.02957, 2019 | 99 | 2019 |
| Dynamic routing between capsules GE Hinton, S Sabour, N Frosst arXiv preprint arXiv:1710.09829, 2017 | 67 | 2017 |
| Darccc: Detecting adversaries by reconstruction from class conditional capsules N Frosst, S Sabour, G Hinton arXiv preprint arXiv:1811.06969, 2018 | 61 | 2018 |
| Dynamic routing between capsules. arXiv 2017 S Sabour, N Frosst, GE Hinton arXiv preprint arXiv:1710.09829, 0 | 60 | |
| Mitigating harm in language models with conditional-likelihood filtration H Ngo, C Raterink, JGM Araújo, I Zhang, C Chen, A Morisot, N Frosst arXiv preprint arXiv:2108.07790, 2021 | 36 | 2021 |
| Interlocking backpropagation: Improving depthwise model-parallelism AN Gomez, O Key, K Perlin, S Gou, N Frosst, J Dean, Y Gal Journal of Machine Learning Research 23 (171), 1-28, 2022 | 22 | 2022 |
| Deflecting adversarial attacks Y Qin, N Frosst, C Raffel, G Cottrell, G Hinton arXiv preprint arXiv:2002.07405, 2020 | 22 | 2020 |
| Smiler: Saliency model implementation library for experimental research C Wloka, T Kunić, I Kotseruba, R Fahimi, N Frosst, NDB Bruce, JK Tsotsos arXiv preprint arXiv:1812.08848, 2018 | 17 | 2018 |
| Matrix capsules with em routing EH Geoffrey, S Sara, F Nicholas International conference on learning representations, 2018 | 10 | 2018 |
| Predicting twitter engagement with deep language models M Volkovs, Z Cheng, M Ravaut, H Yang, K Shen, JP Zhou, A Wong, ... Proceedings of the Recommender Systems Challenge 2020, 38-43, 2020 | 7 | 2020 |
| No news is good news: A critique of the one billion word benchmark H Ngo, JGM Araújo, J Hui, N Frosst arXiv preprint arXiv:2110.12609, 2021 | 6 | 2021 |
| Capsule neural networks GE Hinton, NMW Frosst, SSR Aghdam US Patent 11,494,609, 2022 | 3 | 2022 |
| Text conditional lyric video generation N Frosst, J Kereliuk, G Kid chap. Machine Learning for Creativity and Design Workshop, 2019 | 3 | 2019 |
| The effects of image padding in saliency algorithms NMW Frosst, C Wloka, J Tsotsos Perception 43 (1), 106-107, 2014 | 2 | 2014 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerified email at cs.toronto.edu
