Automated Machine Learning with scikit-learn

A PyTorch Library for Meta-learning Research

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

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

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

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

A PyTorch implementation of OpenAI's REPTILE algorithm

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

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

Personalizing Dialogue Agents via Meta-Learning

Python Meta-Feature Extractor package.

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

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

MetaTS | Time Series Forecasting using Meta Learning

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

Taking causal inference to the extreme!

Meta Learning for LSTM

Implementation of Jump-Start Reinforcement Learning (JSRL) with Stable Baselines3

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

Generalizing to New Physical Systems via Context-Informed Dynamics Model