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 2376–2400 of 3569 papers

TitleStatusHype
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity—0
A novel meta-learning initialization method for physics-informed neural networks—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Any-Way Meta Learning—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy—0
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation—0
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified—0
Approximating Nash Equilibria in General-Sum Games via Meta-Learning—0
A Preliminary Study on Leveraging Meta Learning Technique for Code-switching Speech Recognition—0
A Preliminary Study on Using Meta-learning Technique for Information Retrieval—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
Arbitrary Order Meta-Learning with Simple Population-Based Evolution—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—0
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms—0
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems—0
A Review on Semi-Supervised Relation Extraction—0
A Nested Bi-level Optimization Framework for Robust Few Shot Learning—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Show:102550
← PrevPage 96 of 143Next →
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