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 1051–1075 of 3569 papers

TitleStatusHype
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning—0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—0
Dif-MAML: Decentralized Multi-Agent Meta-Learning—0
Alpha MAML: Adaptive Model-Agnostic Meta-Learning—0
Difficulty-aware Meta-learning for Rare Disease Diagnosis—0
Differentially Private Meta-Learning—0
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates—0
Adaptive Gradient-Based Meta-Learning Methods—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Function Contrastive Learning of Transferable Meta-Representations—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Differentiable Meta-Learning of Bandit Policies—0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
Meta-Learning Bandit Policies by Gradient Ascent—0
Fully Online Meta-Learning Without Task Boundaries—0
Differentiable Bandit Exploration—0
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network—0
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
A CMDP-within-online framework for Meta-Safe Reinforcement Learning—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—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