| Deep generative image models using a laplacian pyramid of adversarial networks E Denton, S Chintala, A Szlam, R Fergus arXiv preprint arXiv:1506.05751, 2015 | 1789 | 2015 |
| Exploiting linear structure within convolutional networks for efficient evaluation E Denton, W Zaremba, J Bruna, Y LeCun, R Fergus arXiv preprint arXiv:1404.0736, 2014 | 1175 | 2014 |
| Unsupervised learning of disentangled representations from video E Denton, V Birodkar arXiv preprint arXiv:1705.10915, 2017 | 329 | 2017 |
| Stochastic video generation with a learned prior E Denton, R Fergus International Conference on Machine Learning, 1174-1183, 2018 | 198 | 2018 |
| Semi-supervised learning with context-conditional generative adversarial networks E Denton, S Gross, R Fergus arXiv preprint arXiv:1611.06430, 2016 | 111* | 2016 |
| ChromoHub: a data hub for navigators of chromatin-mediated signalling L Liu, XT Zhen, E Denton, BD Marsden, M Schapira Bioinformatics 28 (16), 2205-2206, 2012 | 73 | 2012 |
| User conditional hashtag prediction for images E Denton, J Weston, M Paluri, L Bourdev, R Fergus Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 70 | 2015 |
| Modeling others using oneself in multi-agent reinforcement learning R Raileanu, E Denton, A Szlam, R Fergus International conference on machine learning, 4257-4266, 2018 | 69 | 2018 |
| How to train a GAN? Tips and tricks to make GANs work S Chintala, E Denton, M Arjovsky, M Mathieu Dec, 2016 | 55 | 2016 |
| A global assessment of cancer genomic alterations in epigenetic mechanisms MA Shah, EL Denton, CH Arrowsmith, M Lupien, M Schapira Epigenetics & chromatin 7 (1), 1-15, 2014 | 52 | 2014 |
| Towards a critical race methodology in algorithmic fairness A Hanna, E Denton, A Smart, J Smith-Loud Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 22 | 2020 |
| Detecting bias with generative counterfactual face attribute augmentation E Denton, B Hutchinson, M Mitchell, T Gebru arXiv preprint arXiv:1906.06439, 2019 | 17 | 2019 |
| Saving face: Investigating the ethical concerns of facial recognition auditing ID Raji, T Gebru, M Mitchell, J Buolamwini, J Lee, E Denton Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 145-151, 2020 | 16 | 2020 |
| Producing higher-quality samples of natural images E Denton, S Chintala, AD Szlam, RD Fergus US Patent 10,319,076, 2019 | 15* | 2019 |
| Learning goal embeddings via self-play for hierarchical reinforcement learning S Sukhbaatar, E Denton, A Szlam, R Fergus arXiv preprint arXiv:1811.09083, 2018 | 11 | 2018 |
| ChromoHub V2: cancer genomics MA Shah, EL Denton, L Liu, M Schapira Bioinformatics 30 (4), 590-592, 2014 | 9 | 2014 |
| Social biases in NLP models as barriers for persons with disabilities B Hutchinson, V Prabhakaran, E Denton, K Webster, Y Zhong, S Denuyl arXiv preprint arXiv:2005.00813, 2020 | 8 | 2020 |
| Bringing the people back in: Contesting benchmark machine learning datasets E Denton, A Hanna, R Amironesei, A Smart, H Nicole, MK Scheuerman arXiv preprint arXiv:2007.07399, 2020 | 5 | 2020 |
| Unintended machine learning biases as social barriers for persons with disabilitiess B Hutchinson, V Prabhakaran, E Denton, K Webster, Y Zhong, S Denuyl ACM SIGACCESS Accessibility and Computing, 1-1, 2020 | 4 | 2020 |
| Diversity and inclusion metrics in subset selection M Mitchell, D Baker, N Moorosi, E Denton, B Hutchinson, A Hanna, ... Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 117-123, 2020 | 4 | 2020 |
