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 2351–2375 of 3569 papers

TitleStatusHype
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Amortized Bayesian Meta-Learning—0
Amortized Proximal Optimization—0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning—0
Analogy-Forming Transformers for Few-Shot 3D Parsing—0
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach—0
Analytic Personalized Federated Meta-Learning—0
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage—0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—0
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
An Erudite Fine-Grained Visual Classification Model—0
A Neural-Symbolic Framework for Mental Simulation—0
An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection—0
A New First-Order Meta-Learning Algorithm with Convergence Guarantees—0
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization—0
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)—0
Angular-Based Word Meta-Embedding Learning—0
An Information-Theoretic Analysis of In-Context Learning—0
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning—0
Self-Programming Artificial Intelligence Using Code-Generating Language Models—0
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises—0
Anomaly Detection with Ensemble of Encoder and Decoder—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