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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/MindSpore-paper-code-3/code8/tree/main/csd

Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

https://github.com/MindSpore-paper-code-3/code9/tree/main/ernie

ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

https://github.com/MindSpore-scientific-2/code-1/tree/main/MVD

MVDG: A Unified Multi-view Framework for Domain Generalization

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MindSpore-scientific-2/code-4/tree/main/HTM-mindspore

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

https://github.com/MindSpore-scientific-2/code-4/tree/main/nystrom-attention

Nystrom Method for Accurate and Scalable Implicit Differentiation

https://github.com/MindSpore-scientific-2/code-5/tree/main/HTM-mindspore

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

https://github.com/MindSpore-scientific-2/code-5/tree/main/nystrom-attention

Nystrom Method for Accurate and Scalable Implicit Differentiation

https://github.com/MindSpore-scientific/code-12/tree/main/FSMAFL

Improving Federated Learning Personalization via Model Agnostic Meta Learning

https://github.com/MindSpore-scientific/code-2/tree/main/wide-minima-density-hypothesis

Dataset Distillation with Infinitely Wide Convolutional Networks

https://github.com/MindSpore-scientific/code-3/tree/main/comparing-the-efficacy

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

https://github.com/MindSpore-scientific/code-5/tree/main/privacy-enhancement

Privacy Enhancement for Cloud-Based Few-Shot Learning

https://github.com/MindSpore-scientific/code-9/tree/main/MVD

MVDG: A Unified Multi-view Framework for Domain Generalization

https://github.com/MinkiJ/SnaTCHer

Few-shot Open-set Recognition by Transformation Consistency

https://github.com/MoritzTaylor/maml-rl-tf2

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

https://github.com/Mushfequr-Rahman/Omniglot_baselines

Deep Triplet Ranking Networks for One-Shot Recognition

https://github.com/Muzammal-Naseer/Intriguing-Properties-of-Vision-Transformers

Intriguing Properties of Vision Transformers

https://github.com/NVIDIA/NeMo-Curator

Language Models are Few-Shot Learners

https://github.com/NeurIPS2020AnonymousSubmission/mesa

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

https://github.com/NilsHellwig/llm-prompting-asqp

Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction

https://github.com/Octavio-Pappalardo/MAML_for_RL_pytorch

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

https://github.com/Octavio-Pappalardo/RL2-implementation-pytorch

Learning to reinforcement learn

https://github.com/Open-Debin/MetaQDA_Pub

Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

https://github.com/PaddlePaddle/PaddleFL

LEAF: A Benchmark for Federated Settings

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/efl

Entailment as Few-Shot Learner

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3

Language Models are Few-Shot Learners

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml

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

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/metaheac

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

https://github.com/Papirapi/Few_shot-learning-for-Object-Detection

Few-shot Object Detection via Feature Reweighting

https://github.com/Phutoast/Learning2RL

Learning to reinforcement learn

https://github.com/ProKil/FewRel

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

https://github.com/Pulkit-Khandelwal/mldg

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/RB609/QML

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

https://github.com/RUCAIBox/LLMBox

Language Models are Few-Shot Learners

https://github.com/RUCAIBox/RecBole

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/RajeevReddyIlavala/Low-Shot-Learning-Learning

Matching Networks for One Shot Learning

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