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 1371–1380 of 3569 papers

TitleStatusHype
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy EvaluationCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
Fast Efficient Hyperparameter Tuning for Policy GradientsCode0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Imbalanced Regression Pipeline RecommendationCode0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Sequential Scenario-Specific Meta Learner for Online RecommendationCode0
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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