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 1676–1700 of 3569 papers

TitleStatusHype
Reinforcement Learning in Practice: Opportunities and Challenges—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
Learning to Generalize across Domains on Single Test SamplesCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Trace norm regularization for multi-task learning with scarce dataCode0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
L2B: Learning to Bootstrap Robust Models for Combating Label NoiseCode1
MetaKG: Meta-learning on Knowledge Graph for Cold-start RecommendationCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
MAML and ANIL Provably Learn Representations—0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
Auto-Lambda: Disentangling Dynamic Task RelationshipsCode1
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
TIML: Task-Informed Meta-Learning for AgricultureCode1
FORML: Learning to Reweight Data for Fairness—0
Tutorial on amortized optimizationCode2
Meta-Learning Hypothesis Spaces for Sequential Decision-making—0
Fully Online Meta-Learning Without Task Boundaries—0
Neural Collaborative Filtering Bandits via Meta Learning—0
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual InformationCode0
Deep Task-Based Analog-to-Digital ConversionCode0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—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