| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... Journal of Machine Learning Research 25 (70), 1-53, 2024 | 2284 | 2024 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1056 | 2023 |
| 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 | 1042 | 2023 |
| Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification L Hou, D Samaras, T Kurc, Y Gao, J Davis, J Saltz Computer Vision and Pattern Recognition, 2016 | 913 | 2016 |
| Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images J Saltz, R Gupta, L Hou, T Kurc, P Singh, V Nguyen, D Samaras, ... Cell reports 23 (1), 181-193. e7, 2018 | 868 | 2018 |
| Least-to-most prompting enables complex reasoning in large language models D Zhou, N Schärli, L Hou, J Wei, N Scales, X Wang, D Schuurmans, C Cui, ... arXiv preprint arXiv:2205.10625, 2022 | 809 | 2022 |
| The flan collection: Designing data and methods for effective instruction tuning S Longpre, L Hou, T Vu, A Webson, HW Chung, Y Tay, D Zhou, QV Le, ... International Conference on Machine Learning, 22631-22648, 2023 | 444 | 2023 |
| Towards expert-level medical question answering with large language models K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, L Hou, K Clark, S Pfohl, ... arXiv preprint arXiv:2305.09617, 2023 | 416 | 2023 |
| Large Language Models Can Self-Improve J Huang, SS Gu, L Hou, Y Wu, X Wang, H Yu, J Han arXiv preprint arXiv:2210.11610, 2022 | 316 | 2022 |
| Large-scale training of shadow detectors with noisily-annotated shadow examples TFY Vicente, L Hou, CP Yu, M Hoai, D Samaras Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 254 | 2016 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 196 | 2024 |
| Squared Earth Mover’s Distance Loss for Training Deep Neural Networks on Ordered-Classes L Hou, CP Yu, D Samaras NIPS workshop Learning on Distributions, Functions, Graphs and Groups, 0 | 188* | |
| Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images L Hou, V Nguyen, AB Kanevsky, D Samaras, TM Kurc, T Zhao, RR Gupta, ... Pattern recognition 86, 188-200, 2019 | 170 | 2019 |
| Robust Histopathology Image Analysis: to Label or to Synthesize? L Hou, A Agarwal, D Samaras, TM Kurc, RR Gupta, JH Saltz Computer Vision and Pattern Recognition (CVPR), 2019 | 142 | 2019 |
| Computer vision and pattern recognition J Shi, C Tomasi Proceedings CVPR 94, 593-600, 1994 | 114 | 1994 |
| Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data C Robinson, L Hou, K Malkin, R Soobitsky, J Czawlytko, B Dilkina, N Jojic Computer Vision and Pattern Recognition (CVPR), 2019 | 108 | 2019 |
| Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer H Le, R Gupta, L Hou, S Abousamra, D Fassler, L Torre-Healy, RA Moffitt, ... The American journal of pathology 190 (7), 1491-1504, 2020 | 102 | 2020 |
| TensorFlow model garden H Yu, C Chen, X Du, Y Li, A Rashwan, L Hou, P Jin, F Yang, F Liu, J Kim, ... Model Garden for TensorFlow., 2020 | 91 | 2020 |
| Unsupervised histopathology image synthesis L Hou, A Agarwal, D Samaras, TM Kurc, RR Gupta, JH Saltz arXiv preprint arXiv:1712.05021, 2017 | 74 | 2017 |
| Convnets with smooth adaptive activation functions for regression L Hou, D Samaras, T Kurc, Y Gao, J Saltz Artificial Intelligence and Statistics, 430-439, 2017 | 65 | 2017 |
Dimitris SamarasStony Brook UniversityDirección de correo verificada de cs.stonybrook.edu
