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 1726–1750 of 3569 papers

TitleStatusHype
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
A Feature Subset Selection Algorithm Automatic Recommendation Method—0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
Learning to Segment Skin Lesions from Noisy Annotations—0
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Learning to Tune XGBoost with XGBoost—0
Learning to Unlearn for Robust Machine Unlearning—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks—0
Constrained Meta Agnostic Reinforcement Learning—0
Learning Unsupervised Learning Rules—0
Learning via Surrogate PAC-Bayes—0
Dynamic Spectral Backpropagation for Efficient Neural Network Training—0
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
Flow to Learn: Flow Matching on Neural Network Parameters—0
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
A Preliminary Study on Using Meta-learning Technique for Information Retrieval—0
Market-Aware Models for Efficient Cross-Market Recommendation—0
Outlier detection using flexible categorisation and interrogative agendas—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