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 1301–1350 of 3569 papers

TitleStatusHype
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin EffectCode0
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
Scalable Meta-Learning with Gaussian Processes—0
Interpretable Meta-Learning of Physical Systems—0
TIDE: Test Time Few Shot Object DetectionCode0
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization—0
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Decoupled DETR For Few-shot Object Detection—0
Unraveling the Control Engineer's Craft with Neural Networks—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing PlatformsCode0
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems—0
Concept-free Causal Disentanglement with Variational Graph Auto-EncoderCode0
Adaptive Optimization Algorithms for Machine Learning—0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning—0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction—0
When Meta-Learning Meets Online and Continual Learning: A Survey—0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Latent Task-Specific Graph Network SimulatorsCode0
Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Learning to Learn for Few-shot Continual Active Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis—0
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept AcquisitionCode0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval—0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction—0
Meta Learning for Multi-View Visuomotor Systems—0
Investigating Relative Performance of Transfer and Meta Learning—0
Meta-Learning Strategies through Value Maximization in Neural Networks—0
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical SystemsCode0
A Survey on Knowledge Editing of Neural Networks—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Episodic Multi-Task Learning with Heterogeneous Neural ProcessesCode0
On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization—0
Contextual Stochastic Bilevel Optimization—0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Knowledge-driven Meta-learning for CSI Feedback—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