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 651–675 of 3569 papers

TitleStatusHype
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning—0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation LearningCode1
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks—0
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—0
Sample Weight Estimation Using Meta-Updates for Online Continual LearningCode0
Meta-Learning for Neural Network-based Temporal Point Processes—0
An Information-Theoretic Analysis of In-Context Learning—0
Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds—0
Learning Universal PredictorsCode2
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR—0
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh RecoveryCode0
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in RecommendationCode0
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process—0
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation—0
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming—0
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing—0
Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks—0
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-M^2DF)Code0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation—0
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
Window Stacking Meta-Models for Clinical EEG ClassificationCode0
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
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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