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 751–800 of 3569 papers

TitleStatusHype
Incremental Few-Shot Learning for Pedestrian Attribute Recognition—0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
Exploring intra-task relations to improve meta-learning algorithms—0
A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Fair Meta-Learning: Learning How to Learn Fairly—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes—0
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction—0
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction—0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access—0
Bayesian Online Meta-Learning—0
Adaptive Physics-informed Neural Networks: A Survey—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation—0
A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming—0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data—0
A Meta-learning based Generalizable Indoor Localization Model using Channel State Information—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors—0
Adaptive Optimization Algorithms for Machine Learning—0
Bayesian meta learning for trustworthy uncertainty quantification—0
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms—0
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
Bayesian Meta-Learning for Few-Shot 3D Shape Completion—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management—0
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Exploring Domain Shift in Extractive Text Summarization—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Adaptive Hierarchical Hyper-gradient Descent—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
A Meta-Learning Approach to One-Step Active Learning—0
A Comprehensive Review of Few-shot Action Recognition—0
A Meta Learning Approach to Grammatical Error Correction—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning—0
Backdoor Attacks on Federated Meta-Learning—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