Nested Collaborative Learning

Nested Collaborative Learning (NCL) is a machine learning approach that improves model performance by combining multiple expert models in a hierarchical structure. Current research focuses on applying NCL to address challenges in diverse fields, including long-tailed visual recognition (using architectures with inter- and intra-expert collaboration), noisy label learning (leveraging compression techniques like nested dropout), and improving the accuracy of models trained on limited data (e.g., using nested variational autoencoders). The resulting enhanced accuracy and robustness of NCL models have significant implications for various applications, such as medical image analysis (e.g., EEG/MEG spike detection), natural language processing (e.g., Arabic semantic similarity), and materials science (e.g., predicting novel compounds).