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 3201–3225 of 3569 papers

TitleStatusHype
TGG: Transferable Graph Generation for Zero-shot and Few-shot LearningCode0
Exploring Domain Shift in Extractive Text Summarization—0
Meta-Learning with Warped Gradient DescentCode0
Deep Learning Theory Review: An Optimal Control and Dynamical Systems PerspectiveCode0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning—0
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms—0
An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises—0
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal DataCode0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Few-Shot Learning with Global Class RepresentationsCode0
Meta Reasoning over Knowledge Graphs—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Optimizing quantum heuristics with meta-learning—0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial AttacksCode0
Learning to Generalize to Unseen Tasks with Bilevel Optimization—0
Meta-Learning Improves Lifelong Relation Extraction—0
MeLU: Meta-Learned User Preference Estimator for Cold-Start RecommendationCode0
Few-Shot Meta-Denoising—0
Meta Learning for Task-Driven Video Summarization—0
ROAM: Recurrently Optimizing Tracking Model—0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference—0
Towards meta-learning for multi-target regression problems—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