Transfer learning speeds up model training by reusing pre-trained models for new tasks. It reduces data needs, enhancing performance and progress in new ML applications. Transfer learning is limited ...

Whereas reinforcement learning has been applied with success to a range of robotic control problems in complex, uncertain environments, reliance on extensive data - typically sourced from simulation ...

Data sparseness is a major limiting factor for deep machine learning. In the natural sciences, data distributions are heterogeneous. For instance, in chemistry and early-phase drug discovery, compound ...