We develop scalable methods for producing conformal Bayesian predictive intervals with finite sample calibration guarantees.

GreedyOur Y with O(n 2) error obtain parameterization (Frank,1956).

We propose a method for knowledge transfer based on a regularization term in our loss function, supervising the sequence of required reasoning operations.

Theyareusually inspired by-andfittedto-experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data.

However,foragiven task instance, the KG, or certain parts of the KG, may not be useful.