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 1561–1570 of 3569 papers

TitleStatusHype
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
A comparison of small sample methods for Handshape RecognitionCode0
Scalable Bayesian Meta-Learning through Generalized Implicit GradientsCode0
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization—0
Towards Unbiased Calibration using Meta-Regularization—0
Autoregressive Conditional Neural ProcessesCode0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—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