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 1251–1275 of 3569 papers

TitleStatusHype
Meta-Learning for Neural Network-based Temporal Point Processes—0
Sample Weight Estimation Using Meta-Updates for Online Continual LearningCode0
An Information-Theoretic Analysis of In-Context Learning—0
Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds—0
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
Window Stacking Meta-Models for Clinical EEG ClassificationCode0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI—0
Any-Way Meta Learning—0
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems—0
Meta-forests: Domain generalization on random forests with meta-learning—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Marginal Debiased Network for Fair Visual Recognition—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
MoML: Online Meta Adaptation for 3D Human Motion Prediction—0
Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing—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