https://github.com/DavideBuffelli/SAME
공식 코드A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
Papers with Code 아카이브
Meta-Learning
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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/DeLightCMU/ElaborativeRehearsal
공식 코드https://github.com/DevinJake/MARL
공식 코드Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
https://github.com/DongHande/AutoDebias
공식 코드https://github.com/Dongqi-Fan/OnePoseTrans
공식 코드One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
https://github.com/DreamH1gh/TACL2024
공식 코드Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
https://github.com/EavnJeong/Event_Modality_Application
공식 코드Expanding Event Modality Applications through a Robust CLIP-Based Encoder
https://github.com/EdwinKim3069/XtarNet
공식 코드XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
https://github.com/Egg-Hu/BiDf-MKD
공식 코드Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
https://github.com/Egg-Hu/PURER
공식 코드Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
https://github.com/ElementAI/baal
공식 코드Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/ElementAI/osaka
공식 코드Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
https://github.com/ElementAI/synbols
공식 코드Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/ElementAI/synbols-benchmarks
공식 코드Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/EliSchwartz/DeltaEncoder
공식 코드Delta-encoder: an effective sample synthesis method for few-shot object recognition
https://github.com/EmptyJackson/groove
공식 코드Discovering Temporally-Aware Reinforcement Learning Algorithms
https://github.com/Emvista/Meta-XAMR-2024
공식 코드https://github.com/EpistasisLab/pennai
공식 코드Evaluating recommender systems for AI-driven biomedical informatics
https://github.com/ExtensityAI/symbolicai
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/FannierPeng/SgVA-CLIP
공식 코드https://github.com/Fantabulous-J/FSMODQA
공식 코드https://github.com/FatemehAskari/MSENet
공식 코드https://github.com/Fei-Long121/DeepBDC
공식 코드Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
https://github.com/Feiyang/MetaEmbedding
공식 코드Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
https://github.com/FerranAlet/modular-metalearning
공식 코드https://github.com/FlyingRoastDuck/MetaCam_DSCE
공식 코드https://github.com/Frankluox/Channel_Importance_FSL
공식 코드https://github.com/Frankluox/FewShotCodeBase
공식 코드Rectifying the Shortcut Learning of Background for Few-Shot Learning
https://github.com/Fritz449/ProtoNER
공식 코드https://github.com/Frozenmad/MetaDelta
공식 코드MetaDelta: A Meta-Learning System for Few-shot Image Classification
https://github.com/GSK-AI/meta-learning-qsar
공식 코드Meta-Learning Initializations for Low-Resource Drug Discovery
https://github.com/GUOYUE-Cynthia/Meta-Learning-ODE-Integrators
공식 코드Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
https://github.com/Gahyeonkim09/AAPL
공식 코드AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
https://github.com/GengDavid/CyCTR
공식 코드https://github.com/GeorgeLuImmortal/RDL-Rationales-centric-Double-robustness-Learning
공식 코드A Rationale-Centric Framework for Human-in-the-loop Machine Learning
https://github.com/GiorgosPanagopoulos/pandemic_tgnn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.