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 1451–1500 of 3569 papers

TitleStatusHype
Writer adaptation for offline text recognition: An exploration of neural network-based methodsCode0
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
Meta Federated Reinforcement Learning for Distributed Resource Allocation—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Meta-Learning Adversarial Bandit Algorithms—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
All in One: Multi-task Prompting for Graph Neural Networks—0
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis—0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
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
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
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
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
SeFNet: Bridging Tabular Datasets with Semantic Feature NetsCode0
Acceleration in Policy Optimization—0
Meta-Learning for Airflow Simulations with Graph Neural Networks—0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection—0
A Hierarchical Bayesian Model for Deep Few-Shot Meta LearningCode0
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
Virtual Node Tuning for Few-shot Node Classification—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
In-Context Learning through the Bayesian PrismCode0
Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP—0
GSHOT: Few-shot Generative Modeling of Labeled GraphsCode0
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate—0
Evolution of Efficient Symbolic Communication Codes—0
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition—0
Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search—0
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference TransformationCode0
Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition—0
Effective Structured Prompting by Meta-Learning and Representative VerbalizerCode0
MetaXLR -- Mixed Language Meta Representation Transformation for Low-resource Cross-lingual Learning based on Multi-Armed BanditCode0
Taylorformer: Probabilistic Modelling for Random Processes including Time SeriesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
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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