| Personalized multitask learning for predicting tomorrow's mood, stress, and health S Taylor, N Jaques, E Nosakhare, A Sano, R Picard IEEE Transactions on Affective Computing 11 (2), 200-213, 2017 | 256 | 2017 |
| Multi-task learning for predicting health, stress, and happiness N Jaques, S Taylor, E Nosakhare, A Sano, R Picard NIPS Workshop on Machine Learning for Healthcare, 2016 | 76 | 2016 |
| Toward assessing and recommending combinations of behaviors for improving health and well-being E Nosakhare, R Picard ACM Transactions on Computing for Healthcare 1 (1), 1-29, 2020 | 33 | 2020 |
| Seagull: An infrastructure for load prediction and optimized resource allocation O Poppe, T Amuneke, D Banda, A De, A Green, M Knoertzer, ... arXiv preprint arXiv:2009.12922, 2020 | 20 | 2020 |
| Probabilistic latent variable modeling for assessing behavioral influences on well-being E Nosakhare, R Picard Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 13 | 2019 |
| Share the tensor tea: how databases can leverage the machine learning ecosystem Y Asada, V Fu, A Gandhi, A Gemawat, L Zhang, D He, V Gupta, ... arXiv preprint arXiv:2209.04579, 2022 | 11 | 2022 |
| 0795 Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students S Taylor, N Jaques, E Nosakhare, A Sano, EB Klerman, RW Picard Journal of Sleep and Sleep Disorders Research 40 (suppl_1), A294-A295, 2017 | 8 | 2017 |
| Strategies to improve few-shot learning for intent classification and slot-filling S Basu, A Sharaf, KIK Chong, A Fischer, V Rohra, M Amoake, ... Proceedings of the Workshop on Structured and Unstructured Knowledge …, 2022 | 4 | 2022 |
| Semi-supervised few-shot intent classification and slot filling S Basu, A Sharaf, A Fischer, V Rohra, M Amoake, H El-Hammamy, ... arXiv preprint arXiv:2109.08754, 2021 | 4 | 2021 |
| Qt interval adaptation to changes in autonomic balance E Nosakhare, GC Verghese, RC Tasker, T Heldt Computing in Cardiology 2014, 605-608, 2014 | 4 | 2014 |
| Karine lp Kiun Chong, Amr Sharaf, Alex Fischer, Vishal Rohra, Michael Amoake, Hazem El-Hammamy, Ehi Nosakhare, Vijay Ramani, and Benjamin Han. 2021 S Basu Semi-supervised few-shot intent classification and slot filling. CoRR, abs …, 0 | 4 | |
| Multi-task Learning for Predicting Health N Jaques, S Taylor, E Nosakhare, A Sano, R Picard Stress, and Happiness 38, 2016 | 3 | 2016 |
| Importance of Sleep Data in Predicting Next-Day Stress, Happiness, and Health in College Students N Jaques, S Taylor, E Nosakhare, A Sano, EB Klerman, R Picard Journal of Sleep and Sleep Disorders Research 40, 0 | 1 | |
| SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content A Gandhi, R Serrao, B Fang, G Antonius, J Hong, TM Nguyen, S Yi, ... arXiv preprint arXiv:2211.04454, 2022 | | 2022 |
| Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling M Amoake, H El-Hammamy, E Nosakhare, V Ramani, B Han SUKI 2022, 17, 2022 | | 2022 |
| Probabilistic latent variable modeling for predicting future well-being and assessing behavioral influences on mood, stress and health E Nosakhare Massachusetts Institute of Technology, 2018 | | 2018 |
| Semi-Supervised Few-Shot Intent Classification and Slot Filling M Amoake, H El-Hammamy, E Nosakhare, V Ramani, B Han | | |
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