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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2075 of 3569 papers

TitleStatusHype
Min-Max Bilevel Multi-objective Optimization with Applications in Machine LearningCode0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
Amortized Proximal Optimization—0
Interpretable Concept-based Prototypical Networks for Few-Shot Learning—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote SensingCode0
Meta-Learning for Simple Regret MinimizationCode0
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs—0
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal ArmsCode0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Trace norm regularization for multi-task learning with scarce dataCode0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
MAML and ANIL Provably Learn Representations—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
FORML: Learning to Reweight Data for Fairness—0
Meta-Learning Hypothesis Spaces for Sequential Decision-making—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