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Meta-Learning

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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/joshfp/one-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/jpablou/Matching-The-Blanks-Ths

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

https://github.com/jsalbert/prototypical-networks

Prototypical Networks for Few-shot Learning

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

Language Models are Few-Shot Learners

https://github.com/junwoopark92/L2T_ww

Learning What and Where to Transfer

https://github.com/jurastm/differentiable_neural_plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/jxbz/agd

Feature Learning in Infinite-Width Neural Networks

https://github.com/kaist-silab/symmetric_replay

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

https://github.com/kaize0409/GPN_Graph-Few-shot

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

https://github.com/kaize0409/meta-gdn

Few-shot Network Anomaly Detection via Cross-network Meta-learning

https://github.com/kakaobrain/kogpt

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

https://github.com/kakaoenterprise/kakaoenterprise.github.io

A Plug-in Method for Representation Factorization in Connectionist Models

https://github.com/karl-hajjar/wide-networks

Feature Learning in Infinite-Width Neural Networks

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/katalinic/l2rl

Learning to reinforcement learn

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

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

https://github.com/kayuksel/generative-opt

Generative Meta-Learning Robust Quality-Diversity Portfolio

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kekmodel/MPL-pytorch

Meta Pseudo Labels

https://github.com/keonlee9420/Daft-Exprt

TADAM: Task dependent adaptive metric for improved few-shot learning

https://github.com/kgkolias/adversarially-motivated-intrinsic-goals-variation

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

https://github.com/kheyer/OTDD

Geometric Dataset Distances via Optimal Transport

https://github.com/khoiucd/LearningToCompare-Tensorflow

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/kieranjwood/trading-momentum-transformer

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

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

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/kmeng01/rome

Editing Factual Knowledge in Language Models

https://github.com/knnaraghi/fewshot

Matching Networks for One Shot Learning

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kridgeway/f-statistic-loss-nips-2018

Learning Deep Disentangled Embeddings with the F-Statistic Loss

https://github.com/ku2482/discor.pytorch

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/ku2482/rljax

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/kuldeep7688/BioMedicalBertNer

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

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/kylitt/res

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

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