| Detreg: Unsupervised pretraining with region priors for object detection A Bar, X Wang, V Kantorov, CJ Reed, R Herzig, G Chechik, A Rohrbach, ... CVPR 2022, 2022 | 135 | 2022 |
| Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey S Zhao, B Li, CJ Reed, P Xu, K Keutzer arXiv preprint arXiv:2002.12169, 2020 | 135 | 2020 |
| Video digests: a browsable, skimmable format for informational lecture videos A Pavel, CJ Reed, B Hartmann, M Agrawala UIST 2014, 573-582, 2014 | 135 | 2014 |
| Region similarity representation learning CJ Reed*, T Xiao*, X Wang, K Keutzer, T Darrell ICCV 2021, 2021 | 129* | 2021 |
| Self-Supervised Pretraining Improves Self-Supervised Pretraining CJ Reed, X Yue, A Nrusimha, S Ebrahimi, V Vijaykumar, R Mao, B Li, ... WACV 2022, 2022 | 113 | 2022 |
| Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning CJ Reed, R Gupta, S Li, S Brockman, C Funk, B Clipp, S Candido, ... ICCV 2023, 2023 | 103 | 2023 |
| Invariant Information Bottleneck for Domain Generalization B Li, Y Shen, Y Wang, W Zhu, CJ Reed, J Zhang, D Li, K Keutzer, H Zhao AAAI 2022, 2022 | 94 | 2022 |
| SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning CJ Reed, S Metzger, A Srinivas, T Darrell, K Keutzer CVPR 2021, 2021 | 74* | 2021 |
| Climate warming increases extreme daily wildfire growth risk in California PT Brown, H Hanley, A Mahesh, C Reed, SJ Strenfel, SJ Davis, ... Nature 621 (7980), 760-766, 2023 | 60 | 2023 |
| Scaling the Indian Buffet Process via Submodular Maximization CJ Reed, Z Ghahramani ICML 2013, 2013 | 42* | 2013 |
| Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery C Agastya, S Ghebremusse, I Anderson, CJ Reed, H Vahabi, ... ICML Climate Change Workshop, 2021 | 20 | 2021 |
| Multi-source Few-shot Domain Adaptation X Yue, Z Zheng, HP Reed, Colorado J, Das, K Keutzer, AS Vincentelli arXiv preprint arXiv:2109.12391, 2021 | 14 | 2021 |
| Real time machine learning to find fast transient radio anomalies: A semi-supervised approach combining detection and RFI excision C Reed, D Thompson, W Majid, K Wagstaff Proc. Int’l Astronomical Union Symp. Time Domain Astronomy, 2011 | 14 | 2011 |
| What’s Trending? Mining Topical Trends in UGC Systems with YouTube as a Case Study C Reed, T Elvers, P Srinivasan KDD, 2011 | 10 | 2011 |
| Refine and represent: Region-to-object representation learning A Gokul, K Kallidromitis, S Li, Y Kato, K Kozuka, T Darrell, CJ Reed arXiv preprint arXiv:2208.11821, 2023 | 7 | 2023 |
| Apple intelligence foundation language models T Gunter, Z Wang, C Wang, R Pang, A Narayanan, A Zhang, B Zhang, ... arXiv preprint arXiv:2407.21075, 2024 | 5 | 2024 |
| Semi‐supervised Eigenbasis novelty detection DR Thompson, WA Majid, CJ Reed, KL Wagstaff Statistical Analysis and Data Mining: The ASA Data Science Journal 6 (3 …, 2013 | 5 | 2013 |
| Semi-supervised novelty detection with adaptive eigenbases, and application to radio transients DR Thompson, WA Majid, CJ Reed, KL Wagstaff Proceedings of the Conference on Intelligent Data Understanding, 2011 | 5 | 2011 |
| Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers K Miao, A Gokul, R Singh, S Petryk, J Gonzalez, K Keutzer, T Darrell, ... arXiv preprint arXiv:2209.03745, 2022 | 4 | 2022 |
| Metacademy C Reed, R Grosse | 4* | 2013 |
