[NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

A collection of Gradient-Based Meta-Learning Algorithms with pytorch

Python codes to implement Q-Net, a meta-learning method for few shot medical image segmentation

Optim4RL is a Jax framework of learning to optimize for reinforcement learning.

MAML and Reptile sine wave regression example in PyTorch

Modular framework for building self-modeling artificial agents with explicit internal state representation and meta-cognitive capabilities. Includes RL, hybrid, and dummy policies with integrated SelfModel monitoring and scientific metrics.

A recurrent neural network surrogate model with few-shot learning strategy for CO2 storage in deep subsurface saline aquifer

Stanford-AI-Professional-Course

Prototypical Network implementation for prototype classes that allow you to make a ranking for a concept

Task Generation Scheme for the Meta-Unsupervised Algorithm

An implementation of Model Agnostic Meta Learning (MAML) algorithm using pytorch