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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/grey-eye/talking-heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/griffbr/VOSVS

Mobile Robot Manipulation using Pure Object Detection

https://github.com/gudgud96/diff-wave-synth

Differentiable Wavetable Synthesis

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/haebeom-lee/metadrop

Meta Dropout: Learning to Perturb Features for Generalization

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

https://github.com/hamedrab/forec

Cross-Market Product Recommendation

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hannight/amulap

Making Pre-trained Language Models Better Few-shot Learners

https://github.com/happen2me/cross-gnn

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/hazyresearch/ama_prompting

Language Models are Few-Shot Learners

https://github.com/hazyresearch/safari

Hungry Hungry Hippos: Towards Language Modeling with State Space Models

https://github.com/hellokevin07/elastictrainer

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/henry-prior/multimodal-rl

Grounded Language Learning Fast and Slow

https://github.com/hfahrudin/reptile_implement_tf2

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

https://github.com/hhl60492/deep-kernel-transfer

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

https://github.com/hieudepchai/BERT_IE

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hmishra2250/NTM-One-Shot-TF

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/hongshengxin/Induction_network

Induction Networks for Few-Shot Text Classification

https://github.com/howardhsu/L-DEM

Lifelong Domain Word Embedding via Meta-Learning

https://github.com/howardhsu/Meta-Open-World-Learning

Open-world Learning and Application to Product Classification

https://github.com/hoyeoplee/pytorch-maml

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

https://github.com/hrdwsong/ProtoNet-Paddle

Matching Networks for One Shot Learning

https://github.com/huang-research-group/globalfsl2019

Few-Shot Learning with Global Class Representations

https://github.com/huggingface/alignment-handbook

Zephyr: Direct Distillation of LM Alignment

https://github.com/huggingface/transformers

LLaMA: Open and Efficient Foundation Language Models

https://github.com/huitailangyz/MGAA

Meta Gradient Adversarial Attack

https://github.com/hw666666666666/BigTransfer

Big Transfer (BiT): General Visual Representation Learning

https://github.com/hy-zpg/DAGAN

Data Augmentation Generative Adversarial Networks

https://github.com/hy-zpg/FIGR

FIGR: Few-shot Image Generation with Reptile

https://github.com/hysonlab/pandemic_tgnn

Transfer Graph Neural Networks for Pandemic Forecasting

https://github.com/iandrover/maml_noisy_contrasive_learner

MAML is a Noisy Contrastive Learner in Classification

https://github.com/ibm-self-serve-assets/metagen-blended-rag

MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

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