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 1401–1450 of 3569 papers

TitleStatusHype
Interactive Graph Convolutional Filtering—0
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning—0
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning—0
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks—0
Meta-learning for model-reference data-driven control—0
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein SimulatorsCode0
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization—0
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning—0
FAM: fast adaptive federated meta-learning—0
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning—0
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
HyperLoRA for PDEs—0
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary citiesCode0
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions—0
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection—0
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?—0
An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning—0
Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human LifespanCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors—0
Cross-heterogeneity Graph Few-shot Learning—0
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Extension of Transformational Machine Learning: Classification Problems—0
A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction—0
Meta-learning in healthcare: A survey—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation—0
Regularizing Neural Networks with Meta-Learning Generative Models—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty—0
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Nonlinear Meta-Learning Can Guarantee Faster Rates—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Forecasting Early with Meta LearningCode0
Exploiting Field Dependencies for Learning on Categorical DataCode0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access—0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning—0
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning HypernetworksCode0
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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