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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/lsa1997/POP

https://github.com/ltruncel/Microsoft_Azure_50daysofudacity

Language Models are Few-Shot Learners

https://github.com/luchris429/purejaxrl

Discovered Policy Optimisation

https://github.com/lucidrains/CoCa-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/HTM-pytorch

Towards mental time travel: a hierarchical memory for reinforcement learning agents

https://github.com/lucidrains/PaLM-jax

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/PaLM-pytorch

PaLM: Scaling Language Modeling with Pathways

https://github.com/lucidrains/flamingo-pytorch

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/lucidrains/omninet-pytorch

OmniNet: Omnidirectional Representations from Transformers

https://github.com/ludwig-ai/ludwig

Ludwig: a type-based declarative deep learning toolbox

https://github.com/lupalab/flowscan

BRUNO: A Deep Recurrent Model for Exchangeable Data

https://github.com/luxusg1/metabu

Hyperparameter Importance Across Datasets

https://github.com/lyes-khacef/GPU-SOM

GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

https://github.com/lyh6560new/p3sum

POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

https://github.com/lzrobots/LearningToCompare_ZSL

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/maPiche/ANML-Project

Learning to Continually Learn

https://github.com/maoyunyao/joint

Learning What to Learn for Video Object Segmentation

https://github.com/marcovirgolin/SimpleGP

Learning a Formula of Interpretability to Learn Interpretable Formulas

https://github.com/mari-linhares/tensorflow-maml

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

https://github.com/mariehane/prototypical-networks

Prototypical Networks for Few-shot Learning

https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/maxzanella/clip-lora

Low-Rank Few-Shot Adaptation of Vision-Language Models

https://github.com/mazpie/mime

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

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/megvii-model/MetaPruning

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

https://github.com/megvii-research/fsce

Frustratingly Simple Few-Shot Object Detection

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mfeurer/rgpe-code-release

Practical Transfer Learning for Bayesian Optimization

https://github.com/mhw32/prototransformer-public

Induction Networks for Few-Shot Text Classification

https://github.com/michaelisc/cluttered-omniglot

One-Shot Instance Segmentation

https://github.com/microsoft/MWSS

Learning to Reweight Examples for Robust Deep Learning

https://github.com/microsoft/UDA

MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

https://github.com/microsoft/mup

Feature Learning in Infinite-Width Neural Networks

https://github.com/microsoft/ptgnn

Typilus: Neural Type Hints

https://github.com/microsoft/tracecodegen

Program Synthesis with Large Language Models

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

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