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Andrew Trask
Andrew Trask
University of Oxford and OpenMined
Adresse e-mail validée de openmined.org - Page d'accueil
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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
13342020
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
4502018
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
3162020
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
2452021
Neural arithmetic logic units
A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom
Advances in neural information processing systems 31, 2018
2222018
Systems and methods for neural language modeling
A Trask, D Gilmore, M Russell
US Patent 10,339,440, 2019
2202019
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
2122015
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
1492021
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
1482018
Grokking deep learning
AW Trask
Simon and Schuster, 2019
1062019
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
902020
Modeling order in neural word embeddings at scale
A Trask, D Gilmore, M Russell
International Conference on Machine Learning, 2266-2275, 2015
642015
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
302021
Beyond privacy trade-offs with structured transparency
A Trask, E Bluemke, B Garfinkel, CG Cuervas-Mons, A Dafoe
arXiv preprint arXiv:2012.08347, 2020
222020
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
112021
Benchmarking differentially private residual networks for medical imagery
S Singh, H Sikka, S Kotti, A Trask
arXiv preprint arXiv:2005.13099, 2020
102020
Privacy-preserving medical image analysis
A Ziller, J Passerat-Palmbach, T Ryffel, D Usynin, A Trask, IDLC Junior, ...
arXiv preprint arXiv:2012.06354, 2020
92020
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
42020
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
32021
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
32018
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