Weassume that our tree divides the bounded reward range[rmin,rmax] uniformly into2d bins at each leveld D. By labelling left branches ofanode by0,and right branches with a1,we can associate aunique binary stringb1:d to any single internal (d < D) or leaf (d = D) node in the tree. Thedth element, when it exists, is denoted asbd. The root node is denoted by empty string . We should note that even though this exponential term might initially seem discouraging, we setD = 3in our experiments and observe no significant improvements for largerD. Algorithm 1 CTREE, performs regression utilizing a tree-based discetization, where nodes are composedofGLNs.

Leveraging data-dependent gating properties of the GLN we are able to estimate prediction uncertainty with effectively zero algorithmic overhead.

This important phenomenon commonly appears in implemented neural network architectures, and also seems to emerge in epoch-wise curves during the training process.

However,theygenerally lose performance inmore realistic scenarios like learning in a continual manner. In contrast, humans can incorporate their prior knowledge to learn new concepts efficiently without forgetting older ones. In this work, we leverage meta-learning to encourage the model to learn how to learn continually. Inspired by human concept learning, we develop agenerative classifier that efficiently uses data-drivenexperience tolearn newconcepts even from fewsamples while being immune to forgetting. Along with cognitiveand theoretical insights, extensiveexperiments onstandard benchmarks demonstrate the effectiveness of the proposed method.