SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Showing 14511475 of 3569 papers

TitleStatusHype
MetaAge: Meta-Learning Personalized Age EstimatorsCode0
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs0
Continual Few-Shot Learning with Adversarial Class Storage0
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning0
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature SpaceCode0
On the Subspace Structure of Gradient-Based Meta-Learning0
MACFE: A Meta-learning and Causality Based Feature Engineering FrameworkCode0
Online Bayesian Meta-Learning for Cognitive Tracking Radar0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
A Large Scale Search Dataset for Unbiased Learning to RankCode1
PAC Prediction Sets for Meta-Learning0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Betty: An Automatic Differentiation Library for Multilevel Optimization0
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
A Unified Meta-Learning Framework for Dynamic Transfer LearningCode0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning0
Adaptive Fine-Grained Sketch-Based Image RetrievalCode0
The least-control principle for local learning at equilibriumCode0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification0
On the Effectiveness of Sentence Encoding for Intent Detection Meta-LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified