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Meta-Learning

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하위 태스크: 3
평가표: 4
보존된 평가 기록: 13

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/iclavera/learning_to_adapt

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/iclavera/meta-mb

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

https://github.com/icoz69/CaNet

CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

https://github.com/icoz69/DeepEMD

Meta-learning with differentiable closed-form solvers

https://github.com/ido90/robustmetarl

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

https://github.com/ido90/roml-maml

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

https://github.com/idsia/neuraldiffeq-fwp

A Modern Self-Referential Weight Matrix That Learns to Modify Itself

https://github.com/ifsheldon/MPL_Lightning

Meta Pseudo Labels

https://github.com/impredicative/irc-rss-feed-bot

Concurrent Meta Reinforcement Learning

https://github.com/infinitemugen/MAML-Pytorch

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

https://github.com/insait-institute/lm-evaluation-harness-bg

Language Models are Few-Shot Learners

https://github.com/itsmeccr/covid19-npis-causal-analysis

Learning Neural Causal Models from Unknown Interventions

https://github.com/jacobandreas/geca

Human few-shot learning of compositional instructions

https://github.com/jaekyeom/mabas

Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/jaesik817/bmaml

Bayesian Model-Agnostic Meta-Learning

https://github.com/jaesik817/bmaml_rl

Bayesian Model-Agnostic Meta-Learning

https://github.com/janvanrijn/openml-pimp

Hyperparameter Importance Across Datasets

https://github.com/jcoreyes/evolvingrl

Evolving Reinforcement Learning Algorithms

https://github.com/jcoreyes/lgpl

Guiding Policies with Language via Meta-Learning

https://github.com/jesbu1/carl

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/jiachangliu/kategpt3

What Makes Good In-Context Examples for GPT-$3$?

https://github.com/jiakangyuan/helixformer

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/jiangshunyu/SMF-GIN

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

https://github.com/jiean001/models_m/tree/main/LEO

Meta-Learning with Latent Embedding Optimization

https://github.com/jim-schwoebel/allie

Ludwig: a type-based declarative deep learning toolbox

https://github.com/jingwu6/handful-of-trials-in-pytorch

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

https://github.com/jjgarau/DAGPolicyGradient

Evolving Reinforcement Learning Algorithms

https://github.com/jnhwkim/cbp

Compact Bilinear Pooling

https://github.com/joe-siyuan-qiao/FewShot-CVPR

Few-Shot Image Recognition by Predicting Parameters from Activations

https://github.com/joeljosephjin/metadrop-pytorch

https://github.com/joey-wang123/Semi-meta

Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions

https://github.com/joeybose/Meta-Graph

Meta-Graph: Few Shot Link Prediction via Meta Learning

https://github.com/johnnyp2587/transfer-learning

QuantNet: Transferring Learning Across Systematic Trading Strategies

https://github.com/jonasrothfuss/pacoh_nn

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

https://github.com/josephkj/eli

Incremental Object Detection via Meta-Learning

https://github.com/joshfp/fellowship.ai

Prototypical Networks for Few-shot Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.