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 2676–2700 of 3569 papers

TitleStatusHype
Universal Policies for Software-Defined MDPs—0
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations—0
Few-shot Sequence Learning with Transformers—0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Learning active learning at the crossroads? evaluation and discussion—0
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
Variable-Shot Adaptation for Online Meta-Learning—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—0
Adaptive Submodular Meta-Learning—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior KnowledgeCode0
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning—0
Cold-start Sequential Recommendation via Meta Learner—0
Portrait Neural Radiance Fields from a Single Image—0
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting—0
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test—0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learningCode0
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-AlignmentCode0
Forecast with Forecasts: Diversity Matters—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