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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-ai/models/tree/master/official/nlp/gpt

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/research/cv/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/mingyue-cheng/crosstimenet

Generative Pretrained Hierarchical Transformer for Time Series Forecasting

https://github.com/minseop-aitrics/FewshotLearning

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

https://github.com/mitmath/matrixcalc

Efficient and Modular Implicit Differentiation

https://github.com/mkshing/Prompt-Tuning

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/ml4sci/symbahep

Linear algebra with transformers

https://github.com/mlaurelli/amdtl/blob/main/README.md

Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI

https://github.com/mlfoundations/open_flamingo

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/mlpc-ucsd/ConstellationNet

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

https://github.com/mocherson/aki_bert

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

https://github.com/modelscope/modelscope

DCT-Net: Domain-Calibrated Translation for Portrait Stylization

https://github.com/moseslichten/TAFSSL

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

https://github.com/msfuxian/DualAttentionNet

Prototypical Networks for Few-shot Learning

https://github.com/msfuxian/MUSIC

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

https://github.com/mtrazzi/two-step-task

Learning to reinforcement learn

https://github.com/mwydmuch/napkinXC

Online probabilistic label trees

https://github.com/mylonasc/tf-gnns

Relational VAE: A Continuous Latent Variable Model for Graph Structured Data

https://github.com/nalexai/hyperlib

Hyperbolic Image Embeddings

https://github.com/namepen/AI_school_proj

Data Augmentation Generative Adversarial Networks

https://github.com/nanzhaogang/contrib/tree/master/application/learning-an-explicit-hyperparameter-prediction-function-conditioned-on-tasks

Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks

https://github.com/naruya/tecnets-pytorch

Task-Embedded Control Networks for Few-Shot Imitation Learning

https://github.com/nathanieljevans/DVGS

Data Valuation using Reinforcement Learning

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/naver-ai/hypermix

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

https://github.com/naver-ai/simseek

FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

https://github.com/ncbi-nlp/NCBI_BERT

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

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/neitzal/learning-to-distill-trajectories

Generalized Inner Loop Meta-Learning

https://github.com/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/neverCV/Baseline_FSL

A Closer Look at Few-shot Classification

https://github.com/nganltp/admicro-LaSO

LaSO: Label-Set Operations networks for multi-label few-shot learning

https://github.com/ngessert/patch-lesion

Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting

https://github.com/nhatch/nhatch.github.io

Truncated Back-propagation for Bilevel Optimization

https://github.com/nicolay-r/reasoning-for-sentiment-analysis-framework

Large Language Models are Zero-Shot Reasoners

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