We then give a practical approximation using neural networks anddemonstrate itsperformance andsampleefficiencyinpractice.

The discovery of neural architectures from simple building blocks is a longstanding goal of Neural Architecture Search (NAS). Hierarchical search spaces are a promising step towards this goal but lack a unifying search space design framework and typically only search over some limited aspect of architectures. In this work, we introduce a unifying search space design framework based on context-free grammars that can naturally and compactly generate expressive hierarchical search spaces that are 100s of orders of magnitude larger than common spaces from the literature. By enhancing and using their properties, we effectively enable search over the complete architecture and can foster regularity. Further, we propose an efficient hierarchical kernel design for a Bayesian Optimization search strategy to efficiently search over such huge spaces.

Under the new analysis, to achieve an -stationary point of the nested problem, it requiresO( 2)samples in total.

We give an algorithm which solves the problem to additiveaccuracy with O(1/) parallel depth and O n2/ work.

Inmost meta-learning methods, tasks areimplicitly related bysharing parameters oroptimizer. We develop a novel meta-learner of this type for prototype based classification, in which a prototype is generated for each class, such that the nearest neighbor search among the prototypes produces an accurate classification.

We also show that MatrixNet respects group relations allowing generalization to group elements of greater word length than in the training set.

Subsequently, we select train-test splits from the same iteration steps. These settings are used throughout our study. For the few-shot experiments, the same pipeline parameters were utilized with changes noted in the respectivesection. However,thefactthatFIDscores are relatively close to another despite large semantic differences between datasets may indicate that FID based on our utilised FID estimator (Sec. Beyond these limits, generic representations learned byself-supervised learning may offerbetter zero-shot generalization,asalsodiscussedonSec.