[10] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TcIr: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022.
[11] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[12] Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, and Kyriakos Sgarbas. A multi-scheme semi-supervised regression approach. Pattern Recognition Letters, 125:758-765, 2019.
[13] Katja Franke, Gabriel Ziegler, Stefan Klöppel, Christian Gaser, Alzheimer's Disease Neuroimaging Initiative, et al. Estimating the age of healthy subjects from t1-weighted mri scans using kernel methods: exploring the influence of various parameters. Neuroimage, 50(3):883-892, 2010.
[14] Christian Gaser, Katja Franke, Stefan Klöppel, Nikolaos Koutsouleris, Heinrich Sauer, and Alzheimer's Disease Neuroimaging Initiative. Brainage in mild cognitive impaired patients: predicting the conversion to alzheimer's disease. PloS one, 8(6):e67346, 2013.
[15] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020.
[16] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[17] Chen-I Hsieh, Kang Zheng, Chihung Lin, Ling Mei, Le Lu, Weijian Li, Fang-Ping Chen, Yirui Wang, Xiaoyun Zhou, Fakai Wang, et al. Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nature communications, 12(1):5472, 2021.
[18] Neal Jean, Sang Michael Xie, and Stefano Ermon. Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance. Advances in Neural Information Processing Systems, 31, 2018.
[19] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020.
[20] Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, and Pheng-Ann Heng. Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model. In BMVC, 2018.
[21] Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, and Pheng-Ann Heng. Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Transactions on Neural Networks and Learning Systems, 32(2):523–534, 2020.
[22] Yi Li, Yiduo Yu, Yiwen Zou, Tianqi Xiang, and Xiaomeng Li. Online easy example mining for weakly-supervised gland segmentation from histology images. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part IV, pages 578-587. Springer, 2022.
[23] Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, and Xiaomeng Li. Calibrating label distribution for class-imbalanced barely-supervised knee segmentation. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VIII, pages 109-118. Springer, 2022.
[24] Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis. Learning nonlinear operators via deeponet based on the universal approximation theorem of operators. Nature machine intelligence, 3(3):218-229, 2021.
[25] Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, and T Yong-Jin Han. Deep kernels with probabilistic embeddings for small-data learning. In Uncertainty in Artificial Intelligence, pages 918-928. PMLR, 2021.
[26] David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curtis P Langlotz, Paul A Heidenreich, Robert A Harrington, David H Liang, Euan A Ashley, et al. Video-based ai for beat-to-beat assessment of cardiac function. Nature, 580(7802):252-256, 2020.
[27] Marin Vlastelica Pogancic, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek. Differentiation of blackbox combinatorial solvers. In International Conference on Learning Representations, 2020.