Uncertainty quantification of molecular property prediction using Bayesian deep learning, Seongok Ryu, Yongchan Kwon and Woo Youn Kim
I had both oral and poster presentations at the workshop on "Machine Learning for Molecules and Materials" at NeurIPS 2018. http://www.quantum-machine.org/workshops/nips2018/
My work was about using Bayesian deep learning to quantify uncertainties in molecular property predictions. I implemented MC-Dropout network with using augmented graph convolutional network and also with using Concrete dropout to determine optimal dropout rate automatically.
Hope this work can give some lessons to who loves molecular applications of deep learning.
