| Package ‘xgboost’ T Chen, T He, M Benesty, V Khotilovich R version 90 (1-66), 40, 2019 | 4628* | 2019 |
| Resnest: Split-attention networks H Zhang, C Wu, Z Zhang, Y Zhu, H Lin, Z Zhang, Y Sun, T He, J Mueller, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 2137 | 2022 |
| Bag of Tricks for Image Classification with Convolutional Neural Networks T He, Z Zhang, H Zhang, Z Zhang, J Xie, M Li arXiv preprint arXiv:1812.01187, 2018 | 1938 | 2018 |
| Deep graph library: A graph-centric, highly-performant package for graph neural networks M Wang, D Zheng, Z Ye, Q Gan, M Li, X Song, J Zhou, C Ma, L Yu, Y Gai, ... arXiv preprint arXiv:1909.01315, 2019 | 1406 | 2019 |
| SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines T He, M Heidemeyer, F Ban, A Cherkasov, M Ester Journal of cheminformatics 9, 1-14, 2017 | 447* | 2017 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... Journal of Machine Learning Research 21 (23), 1-7, 2020 | 250 | 2020 |
| Higgs boson discovery with boosted trees T Chen, T He JMLR: Workshop and Conference Proceedings 42, 2015 | 240 | 2015 |
| Bag of freebies for training object detection neural networks Z Zhang, T He, H Zhang, Z Zhang, J Xie, M Li arXiv preprint arXiv:1902.04103, 2019 | 239 | 2019 |
| Bridging the gap to real-world object-centric learning M Seitzer, M Horn, A Zadaianchuk, D Zietlow, T Xiao, CJ Simon-Gabriel, ... arXiv preprint arXiv:2209.14860, 2022 | 132 | 2022 |
| Hallucination of multimodal large language models: A survey Z Bai, P Wang, T Xiao, T He, Z Han, Z Zhang, MZ Shou arXiv preprint arXiv:2404.18930, 2024 | 107 | 2024 |
| xgboost: Extreme gradient boosting. R package version 1.6. 0.1 T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ... | 100 | 2022 |
| & Zhou, T.(2015) T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho Xgboost: extreme gradient boosting. R package version 0.4-2 1 (4), 1-4, 0 | 94 | |
| Progressive coordinate transforms for monocular 3d object detection L Wang, L Zhang, Y Zhu, Z Zhang, T He, M Li, X Xue Advances in Neural Information Processing Systems 34, 13364-13377, 2021 | 86 | 2021 |
| Improving semantic segmentation via efficient self-training Y Zhu, Z Zhang, C Wu, Z Zhang, T He, H Zhang, R Manmatha, M Li, ... IEEE transactions on pattern analysis and machine intelligence 46 (3), 1589-1602, 2021 | 76 | 2021 |
| LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation J Cheng, X Liang, X Shi, T He, T Xiao, M Li arXiv preprint arXiv:2302.08908, 2023 | 62 | 2023 |
| Learning hierarchical graph neural networks for image clustering Y Xing, T He, T Xiao, Y Wang, Y Xiong, W Xia, D Wipf, Z Zhang, S Soatto Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 52 | 2021 |
| Grin: Generative relation and intention network for multi-agent trajectory prediction L Li, J Yao, L Wenliang, T He, T Xiao, J Yan, D Wipf, Z Zhang Advances in Neural Information Processing Systems 34, 27107-27118, 2021 | 47 | 2021 |
| Convolution meets lora: Parameter efficient finetuning for segment anything model Z Zhong, Z Tang, T He, H Fang, C Yuan arXiv preprint arXiv:2401.17868, 2024 | 41 | 2024 |
| Learning manifold dimensions with conditional variational autoencoders Y Zheng, T He, Y Qiu, DP Wipf Advances in Neural Information Processing Systems 35, 34709-34721, 2022 | 22 | 2022 |
| Dynamic mini-batch sgd for elastic distributed training: Learning in the limbo of resources H Lin, H Zhang, Y Ma, T He, Z Zhang, S Zha, M Li arXiv preprint arXiv:1904.12043, 2019 | 22 | 2019 |
Mu LiAmazonPotvrđena adresa e-pošte na amazon.com