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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/webis-de/acl-24

LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback

https://github.com/welkincici/MatchingNet

Matching Networks for One Shot Learning

https://github.com/why862555625/-

Learning What and Where to Transfer

https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling

Spatial Deep Learning for Wireless Scheduling

https://github.com/woaixuexixuexi/PSANet

One-Shot Learning for Semantic Segmentation

https://github.com/wu-zhonghua/MM-Net

Learning Meta-class Memory for Few-Shot Semantic Segmentation

https://github.com/x-lance/neusym-rag

Language Models are Few-Shot Learners

https://github.com/xcmax/FewShotWithoutForgetting

Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xinshi-chen/MetaParticleFlow

Particle Flow Bayes' Rule

https://github.com/xinshi-chen/ParticleFlowBayesRule

Particle Flow Bayes' Rule

https://github.com/xinshi-chen/l2stop

Learning to Stop While Learning to Predict

https://github.com/xiuyu0000/new_papers_codes/tree/main/FSMAFL

Improving Federated Learning Personalization via Model Agnostic Meta Learning

https://github.com/xiuyu0000/new_papers_codes/tree/main/MAML

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

https://github.com/xmed-lab/epl_semidg

Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

https://github.com/xpitfire/symbolicai

SymbolicAI: A framework for logic-based approaches combining generative models and solvers

https://github.com/xshiguangdaoliux/qlib

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

https://github.com/xuhongzuo/DeepOD

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xxxnhb/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/xxxnhb/fewshot-egnn

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yangalan123/amortized-interpretability

Can Explanations Be Useful for Calibrating Black Box Models?

https://github.com/yangcedrus/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/yangfeifei02/HOSP

High-order structure preserving graph neural network for few-shot learning

https://github.com/yangsenius/learning-to-learn-by-pytorch

Learning to learn by gradient descent by gradient descent

https://github.com/yangyangyang127/tfs3d

No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

https://github.com/yangyucheng000/MSpaper/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/yangyucheng000/University/tree/main/model-3/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/yannbouteiller/gym-airsimdroneracinglab

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

https://github.com/yaoyao-liu/mini-imagenet-tools

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

https://github.com/yi-han/BERT_Relation_Extraction

Matching the Blanks: Distributional Similarity for Relation Learning

https://github.com/yihong-chen/refactorgnn

Improving Language Plasticity via Pretraining with Active Forgetting

https://github.com/yijiuzai/Matching-Networks-for-One-Shot-Learning

Matching Networks for One Shot Learning

https://github.com/yining610/ProtSi-Network

ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation

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