Model Uncertainty based Active Learning on Tabular Data using Boosted Trees

no code implementations • 30 Oct 2023 • Sharath M Shankaranarayana

Leveraging this model uncertainty, we propose an uncertainty based sampling in active learning for regression tasks on tabular data.

Active Learning

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

no code implementations • 16 Feb 2022 • Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group

The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.

Attention Augmented Convolutional Transformer for Tabular Time-series

no code implementations • 5 Oct 2021 • Sharath M Shankaranarayana, Davor Runje

Time-series classification is one of the most frequently performed tasks in industrial data science, and one of the most widely used data representation in the industrial setting is tabular representation.

Language Modelling Quantization +4