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 3101–3150 of 3569 papers

TitleStatusHype
Invariant Meta Learning for Out-of-Distribution Generalization—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Investigating Relative Performance of Transfer and Meta Learning—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
IPNET:Influential Prototypical Networks for Few Shot Learning—0
Is Fast Adaptation All You Need?—0
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?—0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
Is Pre-training Truly Better Than Meta-Learning?—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?—0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
Joint autoencoders: a flexible meta-learning framework—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Knowledge-driven Meta-learning for CSI Feedback—0
Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils—0
Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning—0
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout—0
Learning to Learn to Compress—0
Labeled Memory Networks for Online Model Adaptation—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features—0
Large-Scale Meta-Learning with Continual Trajectory Shifting—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization—0
LEAF: A Benchmark for Federated Settings—0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learning a Better Initialization for Soft Prompts via Meta-Learning—0
Learning Abstract Task Representations—0
Learning active learning at the crossroads? evaluation and discussion—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Learning Attentive Meta-Transfer—0
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Learning Differential Operators for Interpretable Time Series Modeling—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Show:102550
← PrevPage 63 of 72Next →
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