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 226–250 of 3569 papers

TitleStatusHype
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
A real-time battle situation intelligent awareness system based on Meta-learning & RNN—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
One-Class Domain Adaptation via Meta-Learning—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning—0
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning—0
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
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Meta-Learning for Physically-Constrained Neural System Identification—0
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
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction ForecastingCode0
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAsCode1
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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