261 citations · 344 across the 13 of their papers we have counts for
5 papers · 1 filter
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda +1
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlyi…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
Weicheng Zhu, Narges Razavian
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of disea…