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 2276–2300 of 3569 papers

TitleStatusHype
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning—0
Adversarial Meta-Learning—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations—0
A Feature Subset Selection Algorithm Automatic Recommendation Method—0
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition—0
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
Agnostic Sharpness-Aware Minimization—0
A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems—0
A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
A Hybrid Model For Grammatical Error Correction—0
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence—0
A Knowledge-Driven Meta-Learning Method for CSI Feedback—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