| Combining recurrent, convolutional, and continuous-time models with linear state space layers A Gu, I Johnson, K Goel, K Saab, T Dao, A Rudra, C Ré Advances in neural information processing systems 34, 572-585, 2021 | 421 | 2021 |
| Hungry hungry hippos: Towards language modeling with state space models DY Fu, T Dao, KK Saab, AW Thomas, A Rudra, C Ré arXiv preprint arXiv:2212.14052, 2022 | 345 | 2022 |
| Protecting bare-metal embedded systems with privilege overlays AA Clements, NS Almakhdhub, KS Saab, P Srivastava, J Koo, S Bagchi, ... 2017 IEEE Symposium on Security and Privacy (SP), 289-303, 2017 | 147 | 2017 |
| Domino: Discovering systematic errors with cross-modal embeddings S Eyuboglu, M Varma, K Saab, JB Delbrouck, C Lee-Messer, J Dunnmon, ... arXiv preprint arXiv:2203.14960, 2022 | 134 | 2022 |
| Self-supervised graph neural networks for improved electroencephalographic seizure analysis S Tang, JA Dunnmon, K Saab, X Zhang, Q Huang, F Dubost, DL Rubin, ... arXiv preprint arXiv:2104.08336, 2021 | 119 | 2021 |
| Towards conversational diagnostic ai T Tu, A Palepu, M Schaekermann, K Saab, J Freyberg, R Tanno, A Wang, ... arXiv preprint arXiv:2401.05654, 2024 | 114 | 2024 |
| Capabilities of gemini models in medicine K Saab, T Tu, WH Weng, R Tanno, D Stutz, E Wulczyn, F Zhang, ... arXiv preprint arXiv:2404.18416, 2024 | 88 | 2024 |
| Cross-modal data programming enables rapid medical machine learning JA Dunnmon, AJ Ratner, K Saab, N Khandwala, M Markert, H Sagreiya, ... Patterns 1 (2), 2020 | 85 | 2020 |
| Weak supervision as an efficient approach for automated seizure detection in electroencephalography K Saab, J Dunnmon, C Ré, D Rubin, C Lee-Messer NPJ digital medicine 3 (1), 59, 2020 | 84 | 2020 |
| Effectively modeling time series with simple discrete state spaces M Zhang, KK Saab, M Poli, T Dao, K Goel, C Ré arXiv preprint arXiv:2303.09489, 2023 | 44 | 2023 |
| ViLMedic: a framework for research at the intersection of vision and language in medical AI J Delbrouck, K Saab, M Varma, S Eyuboglu, P Chambon, J Dunnmon, ... Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 35 | 2022 |
| Observational supervision for medical image classification using gaze data K Saab, SM Hooper, NS Sohoni, J Parmar, B Pogatchnik, S Wu, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 33 | 2021 |
| Doubly weak supervision of deep learning models for head CT K Saab, J Dunnmon, R Goldman, A Ratner, H Sagreiya, C Ré, D Rubin Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 28 | 2019 |
| A multivariate adaptive gradient algorithm with reduced tuning efforts S Saab Jr, K Saab, S Phoha, M Zhu, A Ray Neural Networks 152, 499-509, 2022 | 26 | 2022 |
| Modeling multivariate biosignals with graph neural networks and structured state space models S Tang, JA Dunnmon, Q Liangqiong, KK Saab, T Baykaner, ... Conference on Health, Inference, and Learning, 50-71, 2023 | 19 | 2023 |
| Advancing multimodal medical capabilities of Gemini L Yang, S Xu, A Sellergren, T Kohlberger, Y Zhou, I Ktena, A Kiraly, ... arXiv preprint arXiv:2405.03162, 2024 | 15 | 2024 |
| Setting the boundaries of COVID-19 lockdown relaxation measures S Saab, M Al Abbas, RN Samaha, R Jaafar, KK Saab, SS Saab Jr Library Hi Tech 39 (3), 873-887, 2021 | 14 | 2021 |
| Shuffled linear regression with erroneous observations SS Saab, KK Saab 2019 53rd annual conference on information sciences and systems (CISS), 1-6, 2019 | 14 | 2019 |
| A positioning system for photodiode device using collocated LEDs SS Saab, KK Saab IEEE Photonics Journal 8 (5), 1-14, 2016 | 12 | 2016 |
| A stochastic Newton-Raphson method with noisy function measurements KK Saab, SS Saab IEEE signal processing letters 23 (3), 361-365, 2015 | 11 | 2015 |
Christopher RéComputer Science, Stanford UniversityE-mail confirmado em cs.stanford.edu