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 201–225 of 3569 papers

TitleStatusHype
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning—0
Analytic Personalized Federated Meta-Learning—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
A Meta-learner for Heterogeneous Effects in Difference-in-Differences—0
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning—0
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer—0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
qNBO: quasi-Newton Meets Bilevel Optimization—0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network—0
Meta-learning of shared linear representations beyond well-specified linear regression—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
In-Context Meta LoRA Generation—0
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning—0
Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management—0
Evaluating Data Influence in Meta Learning—0
Tailored Forecasting from Short Time Series via Meta-learning—0
Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction PredictionCode0
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning—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