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 1751–1775 of 3569 papers

TitleStatusHype
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Forecast with Forecasts: Diversity Matters—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification—0
Memory-Reduced Meta-Learning with Guaranteed Convergence—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
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
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
Learn To Learn More Precisely—0
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks—0
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning—0
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling—0
Effective Meta-Regularization by Kernelized Proximal Regularization—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
Leveraging Language for Accelerated Learning of Tool Manipulation—0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News—0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios—0
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks—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