metalearning

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)

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

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

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

Personalizing Dialogue Agents via Meta-Learning

Python Meta-Feature Extractor package.

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

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

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

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

Meta Learning for LSTM

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

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

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.

Latency Estimation for Neural Network Architecture

Implementation of "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Hyperparameter tuning via gradient descent

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

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.

A toy project on a Automated Machine Learning technique called linear meta learning

Predict the bugs, features, and questions based on GitHub text data.

Pseudocode Implementation of DAML, Domain Adaptive Dialog Generation via Meta Learning

Master's Project

MAML implementation in PyTorch.