| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 947 | 2023 |
| An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ... NPJ digital medicine 4 (1), 80, 2021 | 133 | 2021 |
| Towards data-driven stroke rehabilitation via wearable sensors and deep learning A Kaku, A Parnandi, A Venkatesan, N Pandit, H Schambra, ... Machine Learning for Healthcare Conference, 143-171, 2020 | 33 | 2020 |
| DARTS: DenseUnet-based automatic rapid tool for brain segmentation A Kaku, CV Hegde, J Huang, S Chung, X Wang, M Young, A Radmanesh, ... arXiv preprint arXiv:1911.05567, 2019 | 33 | 2019 |
| Intermediate layers matter in momentum contrastive self supervised learning A Kaku, S Upadhya, N Razavian Advances in Neural Information Processing Systems 34, 24063-24074, 2021 | 25 | 2021 |
| Deep probability estimation S Liu, A Kaku, W Zhu, M Leibovich, S Mohan, B Yu, H Huang, L Zanna, ... arXiv preprint arXiv:2111.10734, 2021 | 15 | 2021 |
| Be like water: Robustness to extraneous variables via adaptive feature normalization A Kaku, S Mohan, A Parnandi, H Schambra, C Fernandez-Granda arXiv preprint arXiv:2002.04019, 2020 | 12 | 2020 |
| PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training A Parnandi, A Kaku, A Venkatesan, N Pandit, A Wirtanen, H Rajamohan, ... PLOS digital health 1 (6), e0000044, 2022 | 10 | 2022 |
| Strokerehab: A benchmark dataset for sub-second action identification A Kaku, K Liu, A Parnandi, HR Rajamohan, K Venkataramanan, ... Advances in neural information processing systems 35, 1671-1684, 2022 | 6 | 2022 |
| Sequence-to-sequence modeling for action identification at high temporal resolution A Kaku, K Liu, A Parnandi, HR Rajamohan, K Venkataramanan, ... arXiv preprint arXiv:2111.02521, 2021 | 5 | 2021 |
| An artificial intelligence system for predicting the deterioration of covid-19 patients in the emergency department. npj Digital Medicine, 4 (1): 80, May 2021 FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S law Jastrzebski, ... ISSN, 0 | 3 | |
| Data-driven quantitation of movement abnormality after stroke A Parnandi, A Kaku, A Venkatesan, N Pandit, E Fokas, B Yu, G Kim, ... Bioengineering 10 (6), 648, 2023 | 2 | 2023 |
| Quantifying impairment and disease severity using AI models trained on healthy subjects B Yu, A Kaku, K Liu, A Parnandi, E Fokas, A Venkatesan, N Pandit, ... npj Digital Medicine 7 (1), 180, 2024 | | 2024 |
| An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzebski, ... arXiv preprint arXiv:2008.01774, 2020 | | 2020 |
| Knee Cartilage Segmentation Using Diffusion-Weighted MRI A Duarte, CV Hegde, A Kaku, S Mohan, JG Raya arXiv preprint arXiv:1912.01838, 2019 | | 2019 |
| Knee Cartilage Segmentation Using Diffusion Weighted A Duarte, CV Hegde, A Kaku, S Mohan arXiv preprint arXiv:1912.01838, 0 | | |
| Scheduling Cross Entropy and Dice Loss for Optimal Training of Segmentation Models CV Hegde, AR Kaku, S Chung, X Wang, YW Lui, N Razavian | | |
| Towards quantitative rehabilitation of stroke patients via deep learning A Kaku | | |
Carlos Fernandez-GrandaCourant Institute and Center for Data Science, New York University在 cims.nyu.edu 的电子邮件经过验证
