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Meta-Learning
Reinforcement Learning (11): Hierarchical RL and Meta-Learning
A deep dive into hierarchical RL (Options, MAXQ, Feudal Networks, goal-conditioned policies) and meta-RL (MAML, FOMAML, RL^2). Covers temporal abstraction, semi-MDPs, manager-worker architectures, second-order …
Transfer Learning (4): Few-Shot Learning
Learn new concepts from a handful of examples. Covers the N-way K-shot protocol, metric learning (Siamese, Prototypical, Matching, Relation networks), meta-learning (MAML, Reptile), episodic training, miniImageNet …
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Represent a neural network as a directed graph (neurons as nodes, weights as edges) and use a GNN to produce permutation-equivariant embeddings. The right symmetry unlocks generalisation prediction, network …