Quantifying uncertainty in deep learning

Develop reliable and well-calibrated methods to quantify uncertainty in deep learning models so that uncertainty-based active learning strategies can be effective in practice, including for training graph neural networks that predict gene expression responses to perturbations such as GEARS.

Background

In the experimental analysis, the authors evaluate uncertainty-based active learning for training GEARS, a graph neural network that predicts post-perturbation gene expression. They find that an uncertainty-driven selection strategy performs poorly, especially in the early cycles when models are weakly trained and randomly initialized.

They attribute this, in part, to the broader challenge of obtaining trustworthy uncertainty estimates in deep learning, noting that this is a recognized unresolved issue. This motivates the need for principled uncertainty quantification methods that can guide data acquisition decisions reliably in such settings.