Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes H Mendes-Soares, T Raveh-Sadka, S Azulay, K Edens, Y Ben-Shlomo, ... JAMA network open 2 (2), e188102-e188102, 2019 | 160 | 2019 |
Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals H Mendes-Soares, T Raveh-Sadka, S Azulay, Y Ben-Shlomo, Y Cohen, ... The American journal of clinical nutrition 110 (1), 63-75, 2019 | 84 | 2019 |
On the implicit bias of initialization shape: Beyond infinitesimal mirror descent S Azulay, E Moroshko, MS Nacson, BE Woodworth, N Srebro, ... International Conference on Machine Learning, 468-477, 2021 | 64 | 2021 |
Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open. 2019; 2: e188102 H Mendes-Soares, T Raveh-Sadka, S Azulay, K Edens, Y Ben-Shlomo, ... | 5 | 2018 |
Holdout sgd: Byzantine tolerant federated learning S Azulay, L Raz, A Globerson, T Koren, Y Afek arXiv preprint arXiv:2008.04612, 2020 | 2 | 2020 |
782-P: Personalized, Machine Learning-Based Nutrition Reduces Diabetes Markers in Type 2 Diabetic Patients YBEN SHLOMO, S Azulay, T Raveh-Sadka, Y Cohen, A Hanemann Diabetes 68 (Supplement_1), 2019 | 1 | 2019 |
Abstract# 249 Advancements in Personalized Nutrition-the Next Forefront in Treating and Preventing Type 2 Diabetes Y Cohen, A Hanemann, T Raveh-Sadka, S Azulay Endocrine Practice 25, 93, 2019 | | 2019 |
BEHAVIORAL HEALTH H Mendes-Soares, T Raveh-Sadka, S Azulay Aging 23 (1), 21-26, 2019 | | 2019 |