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 2801–2825 of 3569 papers

TitleStatusHype
Bayesian Online Meta-Learning—0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms—0
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning—0
Learning to Adapt Multi-View Stereo by Self-Supervision—0
Domain Generalization via Semi-supervised Meta LearningCode0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
A Meta-learning based Distribution System Load Forecasting Model Selection Framework—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Unfairness Discovery and Prevention For Few-Shot Regression—0
Fair Meta-Learning For Few-Shot Classification—0
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules—0
Adaptive Meta-Learning for Identification of Rover-Terrain DynamicsCode0
Learning Soft Labels via Meta Learning—0
Hidden Incentives for Auto-Induced Distributional Shift—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)—0
Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks—0
Using Sensory Time-cue to enable Unsupervised Multimodal Meta-learning—0
SML: Semantic Meta-learning for Few-shot Semantic Segmentation—0
Teaching to Learn: Sequential Teaching of Agents with Inner States—0
A Markov Decision Process Approach to Active Meta Learning—0
Meta-learning based Alternating Minimization Algorithm for Non-convex OptimizationCode0
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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