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 30513075 of 3569 papers

TitleStatusHype
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal ResourcesCode0
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot LearningCode1
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning0
Meta Label Correction for Noisy Label LearningCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Learning to reinforcement learn for Neural Architecture SearchCode0
Shaping Visual Representations with Language for Few-shot ClassificationCode0
Fair Meta-Learning: Learning How to Learn Fairly0
Addressing Ambiguity of Emotion Labels Through Meta-Learning0
Learning One-Shot Imitation from Humans without HumansCode0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach0
Seeded self-play for language learning0
Hierarchical Expert Networks for Meta-Learning0
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
When MAML Can Adapt Fast and How to Assist When It Cannot0
Meta-Learning to Cluster0
Meta-learning for robust child-adult classification from speech0
HIDRA: Head Initialization across Dynamic targets for Robust ArchitecturesCode0
Meta Learning for End-to-End Low-Resource Speech Recognition0
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event DetectionCode0
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement LearningCode2
Speaker Adaptive Training using Model Agnostic Meta-LearningCode0
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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