Andrew Dai
Cited by
Cited by
Generating sentences from a continuous space
SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio
Proceedings of the 20th SIGNLL Conference on Computational Natural Language …, 2016
Scalable and accurate deep learning with electronic health records
A Rajkomar, E Oren, K Chen, AM Dai, N Hajaj, M Hardt, PJ Liu, X Liu, ...
NPJ Digital Medicine 1 (1), 1-10, 2018
Semi-supervised sequence learning
AM Dai, QV Le
Advances in neural information processing systems 28, 3079-3087, 2015
D Ha, A Dai, QV Le
Proceedings of the International Conference on Learning Representations, 2017
Natural questions: a benchmark for question answering research
T Kwiatkowski, J Palomaki, O Redfield, M Collins, A Parikh, C Alberti, ...
Transactions of the Association for Computational Linguistics 7, 453-466, 2019
Adversarial Training Methods for Semi-Supervised Text Classification
T Miyato, AM Dai, I Goodfellow
Proceedings of the International Conference on Learning Representations, 2017
Document embedding with paragraph vectors
AM Dai, C Olah, QV Le
NIPS 2014 Deep learning workshop, 2015
Maskgan: better text generation via filling in the_
W Fedus, I Goodfellow, AM Dai
arXiv preprint arXiv:1801.07736, 2018
Music transformer
CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ...
arXiv preprint arXiv:1809.04281, 2018
Many paths to equilibrium: GANs do not need to decrease a divergence at every step
W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ...
arXiv preprint arXiv:1710.08446, 2017
Learning longer-term dependencies in rnns with auxiliary losses
T Trinh, A Dai, T Luong, Q Le
International Conference on Machine Learning, 4965-4974, 2018
Who said what: Modeling individual labelers improves classification
M Guan, V Gulshan, A Dai, G Hinton
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
Gmail smart compose: Real-time assisted writing
MX Chen, BN Lee, G Bansal, Y Cao, S Zhang, J Lu, J Tsay, Y Wang, ...
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
Embedding text in hyperbolic spaces
B Dhingra, CJ Shallue, M Norouzi, AM Dai, GE Dahl
arXiv preprint arXiv:1806.04313, 2018
Wearable sensors for Parkinson’s disease: which data are worth collecting for training symptom detection models
L Lonini, A Dai, N Shawen, T Simuni, C Poon, L Shimanovich, ...
NPJ digital medicine 1 (1), 1-8, 2018
Language-independent compound splitting with morphological operations
K Macherey, AM Dai, D Talbot, AC Popat, F Och
Learning the graphical structure of electronic health records with graph convolutional transformer
E Choi, Z Xu, Y Li, M Dusenberry, G Flores, E Xue, A Dai
Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 606-613, 2020
Analyzing the role of model uncertainty for electronic health records
MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, ...
Proceedings of the ACM Conference on Health, Inference, and Learning, 204-213, 2020
An improved relative self-attention mechanism for transformer with application to music generation
CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, C Hawthorne, AM Dai, ...
The supervised hierarchical Dirichlet process
AM Dai, AJ Storkey
IEEE transactions on pattern analysis and machine intelligence 37 (2), 243-255, 2014
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