Federated learning for predicting clinical outcomes in patients with COVID-19 I Dayan, HR Roth, A Zhong, A Harouni, A Gentili, AZ Abidin, A Liu, ... Nature medicine 27 (10), 1735-1743, 2021 | 533 | 2021 |
Deep Learning on FPGAs: Past, Present, and Future G Lacey, GW Taylor, S Areibi arXiv preprint arXiv:1602.04283, 2016 | 250 | 2016 |
Learning human identity from motion patterns N Neverova, C Wolf, G Lacey, L Fridman, D Chandra, B Barbello, G Taylor IEEE Access 4, 1810-1820, 2016 | 225 | 2016 |
Caffeinated FPGAs: FPGA framework for convolutional neural networks R DiCecco, G Lacey, J Vasiljevic, P Chow, G Taylor, S Areibi 2016 International Conference on Field-Programmable Technology (FPT), 265-268, 2016 | 157 | 2016 |
Deep learning architectures for soil property prediction M Veres, G Lacey, GW Taylor 2015 12th Conference on Computer and Robot Vision, 8-15, 2015 | 60 | 2015 |
Stochastic layer-wise precision in deep neural networks G Lacey, GW Taylor, S Areibi arXiv preprint arXiv:1807.00942, 2018 | 21 | 2018 |
Deep learning on fpgas: Past, present, and future. arXiv 2016 G Lacey, GW Taylor, S Areibi arXiv preprint arXiv:1602.04283, 0 | 14 | |
Deep Learning on FPGAs: Past, Present, and Future.(2016) G Lacey, GW Taylor, S Areibi arXiv preprint arXiv:1602.04283, 2016 | 7 | 2016 |
e Silva PMC I Dayan, HR Roth, A Zhong, A Harouni, A Gentili, AZ Abidin, A Liu, ... | 6 | 2021 |
Deep Learning on FPGAs: Past G Lacey, GW Taylor, S Areibi Present, and Future, 2016 | 5 | 2016 |
Deep learning on FPGAs GJ Lacey University of Guelph, 2016 | 5 | 2016 |
Deep learning on FPGAs: past, present, and future. CoRR abs/1602.04283 (2016) G Lacey, GW Taylor, S Areibi | 4 | 2016 |
Federated learning for predicting clinical outcomes in covid-19 patients I Dayan, H Roth, A Zhong, A Harouni, A Gentili, A Abidin, A Liu, AB Costa, ... Nature Medicine, 2021 | 1 | 2021 |
Collaborators N Neverova, C Wolf, G Lacey | | 2015 |