Learning with Small Data - Part 2
Explore advanced techniques for learning with small datasets in this 25-minute conference talk from KDD 2020. Dive into practical examples, including predicting ball trajectories and global temperatures, to understand how to combine machine learning models with domain knowledge. Learn about residual modeling, regularization techniques, and methods for incorporating energy conservation principles. Discover strategies for pre-processing, post-processing, and utilizing unlabeled data to enhance model performance when working with limited training samples. Gain insights from experts Huaxiu Yao, Xiaowei Jia, Vipin Kumar, and Zhenhui Li as they demonstrate how to leverage domain-specific knowledge to improve predictions and overcome the challenges of small data scenarios.