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 1126–1150 of 3569 papers

TitleStatusHype
Meta Learning Text-to-Speech Synthesis in over 7000 Languages—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Graph Mining under Data scarcity—0
Cooperative Meta-Learning with Gradient AugmentationCode0
Meta-learning for Positive-unlabeled Classification—0
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning—0
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization—0
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property—0
Model-Agnostic Zeroth-Order Policy Optimization for Meta-Learning of Ergodic Linear Quadratic Regulators—0
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained ModelsCode0
A CMDP-within-online framework for Meta-Safe Reinforcement Learning—0
Unsupervised Meta-Learning via In-Context Learning—0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Alzheimer's Magnetic Resonance Imaging Classification Using Deep and Meta-Learning Models—0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Meta Reinforcement Learning for Resource Allocation in Multi-Antenna UAV Network with Rate Splitting Multiple Access—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
On the Performance of Unmanned Aerial Vehicles with MIMO VLC—0
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks—0
Beyond Traditional Single Object Tracking: A Survey—0
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications—0
On-device Online Learning and Semantic Management of TinyML SystemsCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
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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