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 1926–1950 of 3569 papers

TitleStatusHype
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Comprehensive Fair Meta-learned Recommender SystemCode0
Deep Meta-learning in Recommendation Systems: A Survey—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Meta-Learning Parameterized SkillsCode0
A Benchmark for Federated Hetero-Task Learning—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
Stacked unsupervised learning with a network architecture found by supervised meta-learning—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-ReptileCode0
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical ImagingCode0
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learningCode0
On the Generalization of Neural Combinatorial Optimization Heuristics—0
Dataset Distillation using Neural Feature RegressionCode0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networksCode0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
Evolving Domain Generalization—0
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
Automatic Short Math Answer Grading via In-context Meta-learningCode0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Meta Representation Learning with Contextual Linear Bandits—0
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectsCode0
Deep Learning with Label Noise: A Hierarchical Approach—0
Meta-Learning Adversarial Bandits—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