maml

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

Memory efficient MAML using gradient checkpointing

TensorFlow 2.0 implementation of MAML.

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

Tools for building raster processing and display services

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

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

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

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.