Andrew Trask
Andrew Trask
DeepMind, University of Oxford, and OpenMined
Verified email at - Homepage
Cited by
Cited by
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
Neural arithmetic logic units
A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom
Advances in Neural Information Processing Systems, 8035-8044, 2018
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
Modeling order in neural word embeddings at scale
A Trask, D Gilmore, M Russell
arXiv preprint arXiv:1506.02338, 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
Grokking deep learning
A Trask
Manning Publications Co., 2019
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
The future of digital health with federated learning
N Rieke, J Hancox, W Li, F Milletari, H Roth, S Albarqouni, S Bakas, ...
arXiv preprint arXiv:2003.08119, 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
Systems and methods for neural language modeling
A Trask, D Gilmore, M Russell
US Patent 10,339,440, 2019
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Articles 1–10