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 1751–1800 of 3569 papers

TitleStatusHype
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity—0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
Deep domain adaptation for polyphonic melody extraction—0
MetaASSIST: Robust Dialogue State Tracking with Meta LearningCode0
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Boosting Natural Language Generation from Instructions with Meta-Learning—0
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-LearningCode0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs—0
Meta Transferring for DeblurringCode0
Neural Routing in Meta LearningCode0
Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations—0
Improving the Reliability for Confidence Estimation—0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
Multi-Modal Fusion by Meta-InitializationCode0
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components—0
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality—0
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps—0
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal PredictionCode0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Meta-Ensemble Parameter Learning—0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Meta-Learning Priors for Safe Bayesian Optimization—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation—0
PersA-FL: Personalized Asynchronous Federated Learning—0
On Stability and Generalization of Bilevel Optimization Problem—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Recurrent Meta-Learning against Generalized Cold-start Problem in CTR PredictionCode0
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification—0
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes—0
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases—0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
Beyond Bayes-optimality: meta-learning what you know you don't know—0
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-LearningCode0
The Curse of Unrolling: Rate of Differentiating Through Optimization—0
Meta-Learning a Cross-lingual Manifold for Semantic ParsingCode0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression—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