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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/supratikp/HOOF

Fast Efficient Hyperparameter Tuning for Policy Gradients

https://github.com/susuxu-s-lab/hierarchical-earthquake-casualty-information-retrieval

Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models

https://github.com/suvarna-kadam/Oct2018_Demo

Few-Shot Learning with Graph Neural Networks

https://github.com/swapnil-ahlawat/Continuous_Meta-Learning_Without_Tasks

Continuous Meta-Learning without Tasks

https://github.com/swapnilagashe/Bi-directional_LSTM_for_NER

Few-shot Learning for Named Entity Recognition in Medical Text

https://github.com/tabpfn-anonym/tabpfnanonym

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/tanhuajie/reason-rft

Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning

https://github.com/tanyuqian/cappy

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

https://github.com/tanyuqian/learning-data-manipulation

Learning to Reweight Examples for Robust Deep Learning

https://github.com/tatsu-lab/stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/teddykoker/learning-to-learn-jax

Learning to learn by gradient descent by gradient descent

https://github.com/teelinsan/camoscio

LLaMA: Open and Efficient Foundation Language Models

https://github.com/temibabs/I-RGCN

Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing

https://github.com/tensorflow/models/tree/master/research/learning_unsupervised_learning

Meta-Learning Update Rules for Unsupervised Representation Learning

https://github.com/tensorfreitas/Siamese-Networks-for-One-Shot-Learning

https://github.com/tginart/sglang

SGLang: Efficient Execution of Structured Language Model Programs

https://github.com/theneuralbeing/maml

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

https://github.com/thuml/Transfer-Learning-Library

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/thunlp/clueanchor

Atlas: Few-shot Learning with Retrieval Augmented Language Models

https://github.com/thunlp/mode-connectivity-plm

CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

https://github.com/tianheyu927/mil

One-Shot Visual Imitation Learning via Meta-Learning

https://github.com/tiehangd/MUPS-EEG

Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

https://github.com/timchen0618/pytorch-leo

Meta-Learning with Latent Embedding Optimization

https://github.com/timonziegenbein/inappropriateness-mitigation

LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback

https://github.com/timy90022/VEGAN

Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects

https://github.com/timy90022/WEGAN

Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects

https://github.com/tizzzzy/demonstration_selection_overview

In-Context Learning with Iterative Demonstration Selection

https://github.com/tjujianyu/rrl

A Closer Look at Few-shot Classification

https://github.com/tobiasvanderwerff/MetaHTR

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/tobyperrett/few-shot-action-recognition

Few-shot Action Recognition with Prototype-centered Attentive Learning

https://github.com/tomas-gajarsky/facetorch

Pre-training strategies and datasets for facial representation learning

https://github.com/tomerraviv95/metadeepsic

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

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

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/tqichun/auto-sklearn-homework

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

https://github.com/tristandeleu/ntm-one-shot

One-shot Learning with Memory-Augmented Neural Networks

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

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

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