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

Showing 17011725 of 3569 papers

TitleStatusHype
Generalized Visual Quality Assessment of GAN-Generated Face Images0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
The Effect of Diversity in Meta-LearningCode0
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Meta-learning Spiking Neural Networks with Surrogate Gradient DescentCode0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images0
Pre-Trained Language Transformers are Universal Image Classifiers0
Synthetic speech detection using meta-learning with prototypical loss0
Meta Learning MDPs with Linear Transition Models0
Meta Learning for Code Summarization0
Cross-Domain Few-Shot Graph ClassificationCode0
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-LearningCode0
Learning Tensor Representations for Meta-Learning0
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling0
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy0
Understanding Few-Shot Multi-Task Representation Learning Theory0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration0
Representation Change in Model-Agnostic Meta-Learning0
OmniPrint: A Configurable Printed Character SynthesizerCode1
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning0
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Graph0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All0
Meta Learning for Natural Language Processing: A Survey0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified