A PyTorch Library for Meta-learning Research

Repository for few-shot learning machine learning projects

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

A dataset of datasets for learning to learn from few examples

Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch

A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.

Personalizing Dialogue Agents via Meta-Learning

Source code for KDD 2020 paper "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation"

Meta learning with BERT as a learner

"모두를 위한 메타러닝" 책에 대한 코드 저장소

TensorFlow 2.0 implementation of MAML.

Memory efficient MAML using gradient checkpointing

Tools for building raster processing and display services

Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"

[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

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

NAACL '24 (Best Demo Paper RunnerUp) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference

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

This repo contains the implementation of some new papers on some advanced topics of machine learning e.g. meta-learning, reinforcement-learning, meta-reinforcement-learning, continual-learning and etc.