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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/egg-hu/lora-recycle

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs

https://github.com/emerald-ai/torchmetal

Torchmeta: A Meta-Learning library for PyTorch

https://github.com/emrgnt-cmplxty/zero-shot-replication

GPT-4 Technical Report

https://github.com/epistasislab/aliro

Evaluating recommender systems for AI-driven biomedical informatics

https://github.com/eric-mitchell/macaw-min

Offline Meta-Reinforcement Learning with Advantage Weighting

https://github.com/eric-mitchell/mend

Editing Factual Knowledge in Language Models

https://github.com/erikalu/class-agnostic-counting

Class-Agnostic Counting

https://github.com/ethz-privsec/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/ethz-spylab/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/exelents/soft-prompt-tuning

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/fabian57fabian/prototypical-networks-few-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/facebookresearch/DomainBed

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/facebookresearch/anli

Language Models are Few-Shot Learners

https://github.com/facebookresearch/chai

LLaMA: Open and Efficient Foundation Language Models

https://github.com/facebookresearch/disentangling-correlated-factors

Learning Deep Disentangled Embeddings with the F-Statistic Loss

https://github.com/facebookresearch/impact-driven-exploration

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

https://github.com/facebookresearch/low-shot-shrink-hallucinate

Matching Networks for One Shot Learning

https://github.com/facebookresearch/metaclip

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

https://github.com/facebookresearch/productive_concept_learning

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

https://github.com/facebookresearch/quip

Few-Shot Named Entity Recognition: A Comprehensive Study

https://github.com/facico/chinese-vicuna

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fajri91/indommlu

Few-shot Learning with Multilingual Language Models

https://github.com/fangpin/siamese-network

https://github.com/farama-foundation/metaworld

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

https://github.com/farhanhubble/omniglot

The Omniglot challenge: a 3-year progress report

https://github.com/farinamatteo/rethinking_fewshot_vlms

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

https://github.com/fastforwardlabs/learning-to-learn

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

https://github.com/few-shot-NER-benchmark/BaselineCode

Few-Shot Named Entity Recognition: A Comprehensive Study

https://github.com/flagalpha/llama2-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flexibility2/Pytorch-Implementation-for-RelationNet

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/floodsung/LearningToCompare_FSL

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/fmeier/online-meta-learning

Online Learning of a Memory for Learning Rates

https://github.com/fmu2/PyTorch-MAML

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

https://github.com/foolyc/Meta-SGD

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

https://github.com/foundation-model-stack/fms-fsdp

PaLM: Scaling Language Modeling with Pathways

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

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