| The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 119, 2020 | 1157 | 2020 |
| A generic framework for privacy preserving deep learning T Ryffel, A Trask, M Dahl, B Wagner, J Mancuso, D Rueckert, ... arXiv preprint arXiv:1811.04017, 2018 | 426 | 2018 |
| Toward trustworthy AI development: mechanisms for supporting verifiable claims M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ... arXiv preprint arXiv:2004.07213, 2020 | 284 | 2020 |
| Neural arithmetic logic units A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom Advances in neural information processing systems 31, 2018 | 208 | 2018 |
| End-to-end privacy preserving deep learning on multi-institutional medical imaging G Kaissis, A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, ... Nature Machine Intelligence 3 (6), 473-484, 2021 | 207 | 2021 |
| Systems and methods for neural language modeling A Trask, D Gilmore, M Russell US Patent 10,339,440, 2019 | 205 | 2019 |
| sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings A Trask, P Michalak, J Liu arXiv preprint arXiv:1511.06388, 2015 | 202 | 2015 |
| Sample efficient adaptive text-to-speech Y Chen, Y Assael, B Shillingford, D Budden, S Reed, H Zen, Q Wang, ... arXiv preprint arXiv:1809.10460, 2018 | 140 | 2018 |
| Pysyft: A library for easy federated learning A Ziller, A Trask, A Lopardo, B Szymkow, B Wagner, E Bluemke, ... Federated Learning Systems: Towards Next-Generation AI, 111-139, 2021 | 121 | 2021 |
| Grokking deep learning AW Trask Simon and Schuster, 2019 | 98 | 2019 |
| Neither private nor fair: Impact of data imbalance on utility and fairness in differential privacy T Farrand, F Mireshghallah, S Singh, A Trask Proceedings of the 2020 workshop on privacy-preserving machine learning in …, 2020 | 66 | 2020 |
| Modeling order in neural word embeddings at scale A Trask, D Gilmore, M Russell International Conference on Machine Learning, 2266-2275, 2015 | 62 | 2015 |
| Dp-sgd vs pate: Which has less disparate impact on model accuracy? A Uniyal, R Naidu, S Kotti, S Singh, PJ Kenfack, F Mireshghallah, A Trask arXiv preprint arXiv:2106.12576, 2021 | 22 | 2021 |
| Beyond privacy trade-offs with structured transparency A Trask, E Bluemke, B Garfinkel, CG Cuervas-Mons, A Dafoe arXiv preprint arXiv:2012.08347, 2020 | 19 | 2020 |
| Syft 0.5: A platform for universally deployable structured transparency AJ Hall, M Jay, T Cebere, B Cebere, KL van der Veen, G Muraru, T Xu, ... arXiv preprint arXiv:2104.12385, 2021 | 9 | 2021 |
| Privacy-preserving medical image analysis A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, IDLC Junior, ... arXiv preprint arXiv:2012.06354, 2020 | 9 | 2020 |
| Benchmarking differentially private residual networks for medical imagery S Singh, H Sikka, S Kotti, A Trask arXiv preprint arXiv:2005.13099, 2020 | 8 | 2020 |
| Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation A Ziller, D Usynin, M Knolle, K Prakash, A Trask, R Braren, M Makowski, ... arXiv preprint arXiv:2107.04265, 2021 | 3 | 2021 |
| Artificial Intelligence in Medicine and Privacy Preservation A Ziller, J Passerat-Palmbach, A Trask, R Braren, D Rueckert, G Kaissis Artificial Intelligence in Medicine, 1-14, 2020 | 3 | 2020 |
| Scaling shared model governance via model splitting M Martic, J Leike, A Trask, M Hessel, S Legg, P Kohli arXiv preprint arXiv:1812.05979, 2018 | 2 | 2018 |
Scott ReedResearch Scientist, Google DeepMindVerified email at google.com
