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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2775 of 3569 papers

TitleStatusHype
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images—0
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation—0
Efficient Meta Learning via Minibatch Proximal Update—0
Efficient meta reinforcement learning via meta goal generation—0
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning—0
Efficient Model Compression Techniques with FishLeg—0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks—0
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Elastically-Constrained Meta-Learner for Federated Learning—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning—0
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation—0
Eliminating Meta Optimization Through Self-Referential Meta Learning—0
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation—0
Enabling Continual Learning in Neural Networks with Meta Learning—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified