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 2176–2200 of 3569 papers

TitleStatusHype
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Wormhole MAML: Meta-Learning in Glued Parameter Space—0
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily—0
Zebra: In-Context and Generative Pretraining for Solving Parametric PDEs—0
Zero-shot meta-learning for small-scale data from human subjects—0
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer—0
Z-Score Normalization, Hubness, and Few-Shot Learning—0
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes—0
Data-Efficient Mutual Information Neural Estimator—0
Incremental Few-Shot Learning for Pedestrian Attribute Recognition—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Test like you Train in Implicit Deep Learning—0
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots—0
Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps—0
Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence—0
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds—0
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions—0
3D Meta-Segmentation Neural Network—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—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