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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/l4fl4m3/bmg

Bootstrapped Meta-Learning

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online

https://github.com/laiviet/ed-fsl-extensive-matching

Extensively Matching for Few-shot Learning Event Detection

https://github.com/laiviet/maml

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

https://github.com/laiyongkui1997/FewJoint

FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

https://github.com/lambert-x/prolab

Language Models are Few-Shot Learners

https://github.com/lampinen/HoMM

Zero-shot task adaptation by homoiconic meta-mapping

https://github.com/lampinen/homm_grids

Zero-shot task adaptation by homoiconic meta-mapping

https://github.com/landerlini/hopaas_client

Hyperparameter Optimization as a Service on INFN Cloud

https://github.com/laohur/LearnToCompareText

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/laohur/RelationNet

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/ldery/automating-auxiliary-learning

Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative

https://github.com/learningtitans/data-depth-design

RECOD Titans at ISIC Challenge 2017

https://github.com/leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification

Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

https://github.com/leezhp1994/TMHFS

A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning

https://github.com/leokarlin/LaSO

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

https://github.com/leokarlin/StarNet

StarNet: towards Weakly Supervised Few-Shot Object Detection

https://github.com/lexisnexis-risk-open-source/ledetection

Frustratingly Simple Few-Shot Object Detection

https://github.com/lflage/openfactscore

GPT-4 Technical Report

https://github.com/lgcollins/tr-maml

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

https://github.com/lhao499/language-quantized-autoencoders

Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment

https://github.com/li-group/optichat

Diagnosing Infeasible Optimization Problems Using Large Language Models

https://github.com/liaoyuhua/GPT-TS

One Fits All:Power General Time Series Analysis by Pretrained LM

https://github.com/lif31up/model-agnostic-meta-learning

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

https://github.com/lif31up/prototypical-network

Prototypical Networks for Few-shot Learning

https://github.com/lihenglin/bridge_training_code

FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning

https://github.com/lindermanlab/S5

Hungry Hungry Hippos: Towards Language Modeling with State Space Models

https://github.com/lishenghui/ARFL

LEAF: A Benchmark for Federated Settings

https://github.com/liujia99/tpm

Masked Autoencoders for Point Cloud Self-supervised Learning

https://github.com/lizonghang/leaf-mx

LEAF: A Benchmark for Federated Settings

https://github.com/llamafamily/llama-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/lmd0311/pointmamba

Masked Autoencoders for Point Cloud Self-supervised Learning

https://github.com/loadder/Dynamic-Meta-filter

Learning Dynamic Alignment via Meta-filter for Few-shot Learning

https://github.com/longhao-chen/aicas2024

Language Models are Few-Shot Learners

https://github.com/longtermrisk/marltoolbox

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

https://github.com/louis2889184/gnn_few_shot_cifar100

Few-Shot Learning with Graph Neural Networks

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