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 876–900 of 3569 papers

TitleStatusHype
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video RecommendationCode0
Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum AlgorithmsCode0
Meta-Learning for Physically-Constrained Neural System Identification—0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations—0
Meta-learning-based percussion transcription and tala identification from low-resource audio—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance—0
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction ForecastingCode0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference—0
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded SmoothnessCode0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition—0
Meta-Learning-Based Delayless Subband Adaptive Filter using Complex Self-Attention for Active Noise Control—0
Automatic Self-supervised Learning for Social Recommendations—0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Learning to Adapt to Low-Resource Paraphrase Generation—0
A Meta-Learning Approach to Bayesian Causal DiscoveryCode0
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion RecognitionCode0
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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