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 1976–2000 of 3569 papers

TitleStatusHype
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach—0
Phase Shift Information Compression in IRS-aided Wireless Systems: Challenges and Opportunities—0
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning—0
p-Meta: Towards On-device Deep Model Adaptation—0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
Population-Based Evolution Optimizes a Meta-Learning Objective—0
Portrait Neural Radiance Fields from a Single Image—0
Practical Conditional Neural Processes Via Tractable Dependent Predictions—0
Practical Conditional Neural Process Via Tractable Dependent Predictions—0
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Pre-Trained Language Transformers are Universal Image Classifiers—0
Pre-training with Meta Learning for Chinese Word Segmentation—0
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs—0
Pre-training Text Representations as Meta Learning—0
Principal component analysis for Gaussian process posteriors—0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration—0
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning—0
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components—0
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Probabilistic Model-Agnostic Meta-Learning—0
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process—0
problexity -- an open-source Python library for binary classification problem complexity assessment—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