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 2901–2950 of 3569 papers

TitleStatusHype
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats—0
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases—0
Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit—0
Few-Shot Named Entity Recognition: An Empirical Baseline Study—0
Few-shot Name Entity Recognition on StackOverflow—0
FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation—0
Few-Shot Object Detection by Attending to Per-Sample-Prototype—0
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin—0
Few-Shot Preference Learning for Human-in-the-Loop RL—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Few-Shot Regression via Learned Basis Functions—0
Few-Shot Regression via Learning Sparsifying Basis Functions—0
Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs—0
Few-shot Sequence Learning with Transformers—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Few-Shot Viewpoint Estimation—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data—0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
FIND:Explainable Framework for Meta-learning—0
Finding online neural update rules by learning to remember—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling—0
Outlier detection using flexible categorisation and interrogative agendas—0
Flow to Learn: Flow Matching on Neural Network Parameters—0
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks—0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Forecast with Forecasts: Diversity Matters—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
FORML: Learning to Reweight Data for Fairness—0
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
From Biased Data to Unbiased Models: a Meta-Learning Approach—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
Fully Online Meta-Learning Without Task Boundaries—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
GEDI: A Graph-based End-to-end Data Imputation Framework—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