| Photorealistic text-to-image diffusion models with deep language understanding C Saharia, W Chan, S Saxena, L Li, J Whang, EL Denton, K Ghasemipour, ... Advances in neural information processing systems 35, 36479-36494, 2022 | 5631 | 2022 |
| Deep generative image models using a laplacian pyramid of adversarial networks EL Denton, S Chintala, R Fergus Advances in neural information processing systems 28, 2015 | 3088 | 2015 |
| Exploiting linear structure within convolutional networks for efficient evaluation EL Denton, W Zaremba, J Bruna, Y LeCun, R Fergus Advances in neural information processing systems 27, 2014 | 2130 | 2014 |
| Unsupervised learning of disentangled representations from video EL Denton Advances in neural information processing systems 30, 2017 | 852 | 2017 |
| Data and its (dis) contents: A survey of dataset development and use in machine learning research A Paullada, ID Raji, EM Bender, E Denton, A Hanna Patterns 2 (11), 2021 | 661 | 2021 |
| Stochastic video generation with a learned prior E Denton, R Fergus International conference on machine learning, 1174-1183, 2018 | 603 | 2018 |
| 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 | 420 | 2020 |
| 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 | 390 | 2020 |
| Towards accountability for machine learning datasets: Practices from software engineering and infrastructure B Hutchinson, A Smart, A Hanna, E Denton, C Greer, O Kjartansson, ... Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 354 | 2021 |
| 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 | 352 | 2020 |
| AI and the everything in the whole wide world benchmark ID Raji, EM Bender, A Paullada, E Denton, A Hanna Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 313 | 2021 |
| 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 | 253 | 2018 |
| Do datasets have politics? Disciplinary values in computer vision dataset development MK Scheuerman, A Hanna, E Denton Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2), 1-37, 2021 | 245 | 2021 |
| On the genealogy of machine learning datasets: A critical history of ImageNet E Denton, A Hanna, R Amironesei, A Smart, H Nicole Big Data & Society 8 (2), 20539517211035955, 2021 | 203 | 2021 |
| Semi-supervised learning with context-conditional generative adversarial networks R Denton, S Gross, R Fergus arXiv preprint arXiv:1611.06430, 2016 | 201 | 2016 |
| Characterising bias in compressed models S Hooker, N Moorosi, G Clark, S Bengio, E Denton arXiv preprint arXiv:2010.03058, 2020 | 190 | 2020 |
| Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research B Koch, E Denton, A Hanna, JG Foster Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 159 | 2021 |
| How to train a GAN? Tips and tricks to make GANs work S Chintala, E Denton, M Arjovsky, M Mathieu GitHub, Dec, 2016 | 135 | 2016 |
| 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 | 124 | 2020 |
| Bringing the people back in: Contesting benchmark machine learning datasets R Denton, A Hanna, R Amironesei, A Smart, H Nicole, MK Scheuerman arXiv preprint arXiv:2007.07399, 2020 | 120 | 2020 |
Rob FergusResearch Scientist, DeepMind. Professor of Computer Science, New York UniversityZweryfikowany adres z cs.nyu.edu