no code implementations • 18 Oct 2023 • Shuai Sun, Jiayun Li, Yilin Mo
This paper is concerned with the finite time identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs.
1 code implementation • 29 Sep 2023 • Jiayun Li, Yuxiao Cheng, Yiwen Lu, Zhuofan Xia, Yilin Mo, Gao Huang
Activation functions are essential to introduce nonlinearity into neural networks, with the Rectified Linear Unit (ReLU) often favored for its simplicity and effectiveness.
no code implementations • 5 Nov 2020 • Jiayun Li, Wenyuan Li, Anthony Sisk, Huihui Ye, W. Dean Wallace, William Speier, Corey W. Arnold
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
no code implementations • 15 May 2020 • Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu, Jackson L. Reese, Azadeh Moghtaderi, Ming-Hsuan Yang, David C. Noelle
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
no code implementations • 18 Oct 2019 • Wenyuan Li, Zichen Wang, Yuguang Yue, Jiayun Li, William Speier, Mingyuan Zhou, Corey W. Arnold
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
no code implementations • 30 May 2019 • Jiayun Li, Wenyuan Li, Arkadiusz Gertych, Beatrice S. Knudsen, William Speier, Corey W. Arnold
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.
no code implementations • 16 May 2019 • Wenyuan Li, Zichen Wang, Jiayun Li, Jennifer Polson, William Speier, Corey Arnold
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
no code implementations • 6 Mar 2019 • Jiayun Li, Mohammad K. Ebrahimpour, Azadeh Moghtaderi, Yen-Yun Yu
Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.