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 3351–3375 of 3569 papers

TitleStatusHype
Base Models for Parabolic Partial Differential EquationsCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Data Valuation using Reinforcement LearningCode0
Meta-learning of Pooling Layers for Character RecognitionCode0
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain GeneralizationCode0
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
On Infinite-Width HypernetworksCode0
Meta-learning of textual representationsCode0
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill PrimitivesCode0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
On the Outsized Importance of Learning Rates in Local Update MethodsCode0
GM-DF: Generalized Multi-Scenario Deepfake DetectionCode0
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)Code0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Dataset Distillation with Infinitely Wide Convolutional NetworksCode0
A Bridge Between Hyperparameter Optimization and Learning-to-learnCode0
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained ModelsCode0
Autoregressive Conditional Neural ProcessesCode0
Using Meta-learning to Recommend Process Discovery MethodsCode0
Open-world Learning and Application to Product ClassificationCode0
Meta-Learning Priors for Efficient Online Bayesian RegressionCode0
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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