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 1926–1950 of 3569 papers

TitleStatusHype
Meta-Learning Framework for End-to-End Imposter Identification in Unseen Speaker Recognition—0
Specialization in Hierarchical Learning Systems—0
SPeCiaL: Self-Supervised Pretraining for Continual Learning—0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
Speech-driven Facial Animation using Cascaded GANs for Learning of Motion and Texture—0
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning—0
Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches—0
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning—0
SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction—0
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications—0
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection—0
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer—0
ST^2: Small-data Text Style Transfer via Multi-task Meta-Learning—0
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms—0
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking—0
Stacked Generalization for Medical Concept Extraction from Clinical Notes—0
Stacked unsupervised learning with a network architecture found by supervised meta-learning—0
Stackelberg Meta-Learning Based Control for Guided Cooperative LQG Systems—0
Stacking and stability—0
Stacking for Probabilistic Short-term Load Forecasting—0
Stacking hybrid GARCH models for forecasting Bitcoin volatility—0
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction—0
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning—0
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees—0
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate—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