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
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
Massive Editing for Large Language Models via Meta LearningCode1
Learning to Learn for Few-shot Continual Active Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning ApplicationsCode1
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis—0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept AcquisitionCode0
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval—0
Investigating Relative Performance of Transfer and Meta Learning—0
Meta Learning for Multi-View Visuomotor Systems—0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction—0
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical SystemsCode0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
A Survey on Knowledge Editing of Neural Networks—0
Meta-Learning Strategies through Value Maximization in Neural Networks—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—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
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUsCode1
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-EncoderCode1
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation ExtractionCode1
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Knowledge-driven Meta-learning for CSI Feedback—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Meta learning with language models: Challenges and opportunities in the classification of imbalanced textCode0
Implicit meta-learning may lead language models to trust more reliable sourcesCode1
Are LSTMs Good Few-Shot Learners?Code0
Towards Subject Agnostic Affective Emotion Recognition—0
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs—0
Unsupervised Representation Learning to Aid Semi-Supervised Meta LearningCode0
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learningCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
Group Preference Optimization: Few-Shot Alignment of Large Language ModelsCode1
Context-Aware Meta-LearningCode1
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot ClassificationCode1
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity RepresentatioCode0
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Dynamic Link Prediction for New Nodes in Temporal Graph Networks—0
A Partially Supervised Reinforcement Learning Framework for Visual Active SearchCode0
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks—0
Subspace Adaptation Prior for Few-Shot LearningCode0
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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