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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/ash3n/Prototypical-Network-TF

Prototypical Networks for Few-shot Learning

https://github.com/automl/auto-sklearn

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

https://github.com/automl/tabpfn

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/automl/transformerscandobayesianinference

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/avivne/bilinear-transduction

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/awasthiabhijeet/Learning-From-Rules

Learning to Reweight Examples for Robust Deep Learning

https://github.com/awjuliani/Meta-RL

Learning to reinforcement learn

https://github.com/awslabs/aws-cv-task2vec

Task2Vec: Task Embedding for Meta-Learning

https://github.com/bachnguyente/temporal-mgn

Transfer Graph Neural Networks for Pandemic Forecasting

https://github.com/bagustris/ExVo2022

The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts

https://github.com/barmayo/spatial_attention

Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

https://github.com/barnrang/Prototypical-network-keras-reimplementation

Prototypical Networks for Few-shot Learning

https://github.com/batorskq/prl

Large Language Models Are Human-Level Prompt Engineers

https://github.com/batsresearch/alfred

LLaMA: Open and Efficient Foundation Language Models

https://github.com/batsresearch/taglets

Big Transfer (BiT): General Visual Representation Learning

https://github.com/bay3s/auto-dr

Solving Rubik's Cube with a Robot Hand

https://github.com/benedekrozemberczki/pytorch_geometric_temporal

Transfer Graph Neural Networks for Pandemic Forecasting

https://github.com/beomi/koalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/bertinetto/r2d2

Meta-learning with differentiable closed-form solvers

https://github.com/bethgelab/InDomainGeneralizationBenchmark

Big Transfer (BiT): General Visual Representation Learning

https://github.com/bhiziroglu/Siamese-Neural-Networks

https://github.com/bigscience-workshop/Megatron-DeepSpeed

Language Models are Few-Shot Learners

https://github.com/bigscience-workshop/biomedical

In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

https://github.com/bigscience-workshop/promptsource

Language Models are Few-Shot Learners

https://github.com/bkhmsi/meta-rl-harlow

Learning to reinforcement learn

https://github.com/blues-lin/fin-analyze

Learning to Continually Learn

https://github.com/bojianhou/oagd

Meta-Learning with Implicit Gradients

https://github.com/brain-bzh/pefsl

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

https://github.com/brilhana/quantnet

QuantNet: Transferring Learning Across Systematic Trading Strategies

https://github.com/buaali/MAN

Multi-Pretext Attention Network for Few-shot Learning with Self-supervision

https://github.com/cactilab/hateguard

Entailment as Few-Shot Learner

https://github.com/calvinmccarter/kditransform

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/carbonati/variational-zoo

Learning Deep Disentangled Embeddings with the F-Statistic Loss

https://github.com/chandan047/MetaLearningForNER

Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

https://github.com/chaoyi-wu/finetune_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/chaoyi-wu/pmc-llama

LLaMA: Open and Efficient Foundation Language Models

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