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20 search results found

Hands On Meta Learning With Python ⭐ 857

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

Reinforcement_learning_tutorial_with_demo ⭐ 357

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

Neural Process Family ⭐ 163

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

Personalizing Dialogue Agents via Meta-Learning

Reptile Pytorch ⭐ 107

A PyTorch implementation of OpenAI's REPTILE algorithm

Hands On One Shot Learning ⭐ 98

This repository is for coding exercises listed in Book Hands on One Shot Learning.

Meta Learning Bert ⭐ 94

Meta learning with BERT as a learner

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

Meta Learning For Everyone ⭐ 79

"모두를 위한 메타러닝" 책에 대한 코드 저장소

AReLU: Attention-based-Rectified-Linear-Unit

Memory Efficient Maml ⭐ 48

Memory efficient MAML using gradient checkpointing

Framework for meta-optimisation in AutoML tasks

Meta Reinforcement Learning ⭐ 26

Code snippets of Meta Reinforcement Learning algorithms

This is official code for ACIIDS2022 paper "Meta-learning and Personalization layer in Federated learning"

Meta Learning Without Memorization ⭐ 17

A study about the following problems: what is the memorization problem in meta-learning; why does memorization problem happen; and how to prevent it. (ICLR 2020)

Ozone Narx Dnn ⭐ 12

Air-quality forecasting in Belgium using Deep Neural Networks, Neuroevolution and distributed Island Transpeciation

Gpt3 In Context Fitting ⭐ 10

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

Learning Scaffold ⭐ 9

This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"

Cs 330 Deep Multi Task And Meta Learning ⭐ 8

https://cs330.stanford.edu/

Esjacobians ⭐ 6

Implementations of the algorithms described in the paper: On the Convergence Theory for Hessian-Free Bilevel Algorithms.

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