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

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Showing 381–390 of 3569 papers

TitleStatusHype
ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuningCode0
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
ADIOS: Antibody Development via Opponent ShapingCode0
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
Online Nonconvex Bilevel Optimization with Bregman Divergences—0
AALF: Almost Always Linear ForecastingCode0
Rethinking Meta-Learning from a Learning Lens—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