Physics Commons^™

Synergy Of Physics-Based Reasoning And Machine Learning In Biomedical Applications: Towards Unlimited Deep Learning With Limited Data, Valeriy Gavrishchaka, Olga Senyukova, Mark Koepke

Faculty & Staff Scholarship

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, …