no code implementations • 16 Oct 2023 • Chuan He, Le Peng, Ju Sun
In practice, many machine learning (ML) problems come with constraints, and their applied domains involve distributed sensitive data that cannot be shared with others, e. g., in healthcare.
2 code implementations • 20 Jul 2023 • Le Peng, Gaoxiang Luo, Sicheng Zhou, jiandong chen, Rui Zhang, Ziyue Xu, Ju Sun
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP).
no code implementations • 23 Mar 2023 • Hengyue Liang, Buyun Liang, Le Peng, Ying Cui, Tim Mitchell, Ju Sun
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
no code implementations • 17 Feb 2023 • Yash Travadi, Le Peng, Xuan Bi, Ju Sun, Mochen Yang
However, the economic considerations of the clients, such as fairness and incentive, are yet to be fully explored.
no code implementations • 21 Oct 2022 • Le Peng, Yash Travadi, Rui Zhang, Ying Cui, Ju Sun
We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification.
2 code implementations • 23 Oct 2021 • Taihui Li, Zhong Zhuang, Hengyue Liang, Le Peng, Hengkang Wang, Ju Sun
Recent works have shown the surprising effectiveness of deep generative models in solving numerous image reconstruction (IR) tasks, even without training data.
2 code implementations • 9 Jun 2021 • Le Peng, Hengyue Liang, Gaoxiang Luo, Taihui Li, Ju Sun
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).
no code implementations • 3 Jun 2021 • Ju Sun, Le Peng, Taihui Li, Dyah Adila, Zach Zaiman, Genevieve B. Melton, Nicholas Ingraham, Eric Murray, Daniel Boley, Sean Switzer, John L. Burns, Kun Huang, Tadashi Allen, Scott D. Steenburg, Judy Wawira Gichoya, Erich Kummerfeld, Christopher Tignanelli
Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms.