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 26–50 of 3569 papers

TitleStatusHype
Meta-SurDiff: Classification Diffusion Model Optimized by Meta Learning is Reliable for Online Surgical Phase Recognition—0
Meta-learning how to Share Credit among Macro-ActionsCode0
Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring—0
Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation—0
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles—0
PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling—0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates—0
A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems—0
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And NormalizationCode0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Detecting Sockpuppetry on Wikipedia Using Meta-LearningCode0
Meta-learning Representations for Learning from Multiple Annotators—0
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches—0
Towards Efficient and Effective Alignment of Large Language Models—0
Test-Time Adaptation for Generalizable Task Progress Estimation—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Meta-Adaptive Prompt Distillation for Few-Shot Visual Question Answering—0
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks—0
Temporal Variational Implicit Neural Representations—0
TSRating: Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment—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