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

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Showing 2401–2450 of 3569 papers

TitleStatusHype
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Meta-learning Amidst Heterogeneity and AmbiguityCode0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Meta-Learning for Relative Density-Ratio Estimation—0
Resilient UAV Swarm Communications with Graph Convolutional Neural NetworkCode0
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-LearningCode0
Meta-learning for Matrix Factorization without Shared Rows or Columns—0
Representation based meta-learning for few-shot spoken intent recognitionCode0
Fast Training of Neural Lumigraph Representations using Meta Learning—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation—0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Multimodal Emergent Fake News Detection via Meta Neural Process Networks—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
Compositional federated learning: Applications in distributionally robust averaging and meta learning—0
Task Attended Meta-Learning for Few-Shot Learning—0
Multi-Pair Text Style Transfer on Unbalanced Data—0
Transformation Invariant Few-Shot Object Detection—0
Person30K: A Dual-Meta Generalization Network for Person Re-Identification—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
How Low Can We Go: Trading Memory for Error in Low-Precision TrainingCode0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
SPeCiaL: Self-Supervised Pretraining for Continual Learning—0
Contextualizing Meta-Learning via Learning to DecomposeCode0
Generative Conversational Networks—0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Meta-Learning for Symbolic Hyperparameter DefaultsCode0
Attentional Meta-learners for Few-shot Polythetic ClassificationCode0
Probabilistic task modelling for meta-learningCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Learning Functional Priors and Posteriors from Data and Physics—0
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization—0
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
RegMix: Data Mixing Augmentation for Regression—0
Meta-Learning Reliable Priors in the Function Space—0
A Meta Learning Approach to Discerning Causal Graph Structure—0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
Meta-learning for downstream aware and agnostic pretraining—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Meta-Learning with Variational Semantic Memory for Word Sense DisambiguationCode0
Minimax and Neyman-Pearson Meta-Learning for Outlier LanguagesCode0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning—0
Modality-specific Distillation—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