no code implementations • 27 Nov 2023 • Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda, Narges Razavian
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data.
1 code implementation • CVPR 2023 • Kangning Liu, Weicheng Zhu, Yiqiu Shen, Sheng Liu, Narges Razavian, Krzysztof J. Geras, Carlos Fernandez-Granda
The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels.
1 code implementation • 23 Mar 2022 • Weicheng Zhu, Carlos Fernandez-Granda, Narges Razavian
The resulting representations and clusters from self-supervision are used as features of a survival model for recurrence prediction at the patient level.
no code implementations • 21 Nov 2021 • Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
2 code implementations • CVPR 2022 • Sheng Liu, Kangning Liu, Weicheng Zhu, Yiqiu Shen, Carlos Fernandez-Granda
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
1 code implementation • 8 Dec 2019 • Weicheng Zhu, Narges Razavian
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
1 code implementation • 20 Jul 2018 • Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
no code implementations • 20 May 2016 • Yusheng Xie, Nan Du, Wei Fan, Jing Zhai, Weicheng Zhu
In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks.