metalearning

learnables/learn2learn

A PyTorch Library for Meta-learning Research

sudharsan13296/Awesome-Meta-Learning

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

sudharsan13296/Hands-On-Meta-Learning-With-Python

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

kjunelee/MetaOptNet

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

g1910/HyperNetworks

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

gabrielhuang/reptile-pytorch

A PyTorch implementation of OpenAI's REPTILE algorithm

dragen1860/MAML-TensorFlow

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

khurramjaved96/mrcl

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

ealcobaca/pymfe

Python Meta-Feature Extractor package.

HLTCHKUST/PAML

Personalizing Dialogue Agents via Meta-Learning

navneet-nmk/Hierarchical-Meta-Reinforcement-Learning

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

hannw/sgrnn

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

DrSasanBarak/metats

MetaTS | Time Series Forecasting using Meta Learning

steventango/jumpstart-rl

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

dscolby/CausalELM.jl

Taking causal inference to the extreme!

ashishpatel26/Meta-Learning-

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

catnlp/metaLSTM

Meta Learning for LSTM

yuan-yin/CoDA

Generalizing to New Physical Systems via Context-Informed Dynamics Model

cvqluu/dropclass_speaker

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

Baijiong-Lin/MOML

[NeurIPS 2021 | AIJ 2024] Multi-Objective Meta Learning

rovle/gpt3-in-context-fitting

Experiments on GPT-3's ability to fit numerical models in-context.

edwin-pan/MetaHDR

Model-Agnostic Meta-Learning for HDR Image Reconstruction. By learning the common structure between all LDR-to-HDR conversion tasks, our model is able to adapt it's predictions given extra exposures of a scene. This novel approach reframes LDR-to-HDR conversion as a meta-learning problem.

seujung/SNAIL-gluon

Implementation of SNAIL(A Simple Neural Attentive Meta-Learner) with Gluon

RobinKa/MetaVAE

Meta-learning by applying MAML to an inner variational auto-encoder to automatically learn generative models with few examples

ealcobaca/meta-padawan

Meta-Padawan solution to the NeurIPS (2021) - Few-shot learning competition.

marlesson/meta-bandit-selector

The Contextual Meta-Bandit (CMB) can be used to select models using the context with online learning based on Reiforcement Learning problem. It's can be used for recommender system ensemble, A/B test, and other dynamic model selector problem.

NonlinearArtificialIntelligenceLab/DiversityNN

Code Repository for "Neural networks embrace diversity" paper

SewoongLab/RobustMetaLearningMLR

This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.

arvintashakori/SemiPFL

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

haghish/autoEnsemble

autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners

Panshark/COLA

Source code for Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation

srikarym/FiLM-for-Meta-Learning

karthik-d/few-shot-dermoscopic-image-analysis

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

XoriieInpottn/maml-pytorch

MAML implementation in PyTorch.

ZhexiongLiu/Task-Adaptive-Meta-Learning

Paper: Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability.