Automated Machine Learning with scikit-learn

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

A PyTorch Library for Meta-learning Research

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

Faster and elegant TensorFlow Implementation of paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

A PyTorch implementation of OpenAI's REPTILE algorithm

PyTorch implementation of HyperNetworks (Ha et al., ICLR 2017) for ResNet (Residual Networks)

Python Meta-Feature Extractor package.

Code for the NeurIPS19 paper "Meta-Learning Representations for Continual Learning"

Personalizing Dialogue Agents via Meta-Learning

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

Meta Learning for LSTM

This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.

MetaTS | Time Series Forecasting using Meta Learning

This repository is the code base for the project titled Latent Embedding Optimization for Few-shot Segmentation.

Meta learning is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments.

SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

Skin lesion image analysis that draws on meta-learning to improve performance in the low data and imbalanced data regimes.