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 2176–2200 of 3569 papers

TitleStatusHype
ST^2: Small-data Text Style Transfer via Multi-task Meta-Learning—0
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms—0
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking—0
Stacked Generalization for Medical Concept Extraction from Clinical Notes—0
Stacked unsupervised learning with a network architecture found by supervised meta-learning—0
Stackelberg Meta-Learning Based Control for Guided Cooperative LQG Systems—0
Stacking and stability—0
Stacking for Probabilistic Short-term Load Forecasting—0
Stacking hybrid GARCH models for forecasting Bitcoin volatility—0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees—0
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate—0
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization—0
Set Based Stochastic Subsampling—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Stress Testing of Meta-learning Approaches for Few-shot Learning—0
Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing—0
Style-Guided Domain Adaptation for Face Presentation Attack Detection—0
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval—0
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning—0
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis—0
Towards Explainable Meta-Learning for DDoS Detection—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction—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