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 826–850 of 3569 papers

TitleStatusHype
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence—0
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning—0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization—0
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug DiscoveryCode0
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies—0
Generalizable speech deepfake detection via meta-learned LoRA—0
Diverse Inference and Verification for Advanced Reasoning—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural RepresentationCode0
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
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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