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 901–950 of 3569 papers

TitleStatusHype
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning HypernetworksCode0
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning—0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
Writer adaptation for offline text recognition: An exploration of neural network-based methodsCode0
OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-LearningCode1
Advances and Challenges in Meta-Learning: A Technical Review—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Semi Supervised Meta Learning for Spatiotemporal Learning—0
MALIBO: Meta-learning for Likelihood-free Bayesian OptimizationCode0
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Meta Federated Reinforcement Learning for Distributed Resource Allocation—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
Meta-Learning Adversarial Bandit Algorithms—0
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
All in One: Multi-task Prompting for Graph Neural Networks—0
Improving Language Plasticity via Pretraining with Active ForgettingCode1
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis—0
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Elastically-Constrained Meta-Learner for Federated Learning—0
AutoML in Heavily Constrained ApplicationsCode0
A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying ModerationCode0
Unsupervised Episode Generation for Graph Meta-learningCode1
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided DiffusionCode1
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception—0
Is Pre-training Truly Better Than Meta-Learning?—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Evading Forensic Classifiers with Attribute-Conditioned Adversarial FacesCode1
SeFNet: Bridging Tabular Datasets with Semantic Feature NetsCode0
Meta-Learning for Airflow Simulations with Graph Neural Networks—0
Acceleration in Policy Optimization—0
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection—0
A Hierarchical Bayesian Model for Deep Few-Shot Meta LearningCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend ForecastingCode1
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation—0
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization—0
Inductive Linear Probing for Few-shot Node Classification—0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats—0
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning—0
Adaptive Multi-Teacher Knowledge Distillation with Meta-LearningCode1
Virtual Node Tuning for Few-shot Node Classification—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