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 576–600 of 3569 papers

TitleStatusHype
Domain Generalization through Meta-Learning: A Survey—0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories—0
Meta Learning in Bandits within Shared Affine Subspaces—0
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-LearningCode1
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning—0
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance EstimationCode0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
A Moreau Envelope Approach for LQR Meta-Policy Estimation—0
Advancing Extrapolative Predictions of Material Properties through Learning to Learn—0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Learning-to-Learn the Wave Angle Estimation—0
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study—0
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent—0
NTK-Guided Few-Shot Class Incremental LearningCode1
Improving Generalization via Meta-Learning on Hard Samples—0
Compositional learning of functions in humans and machines—0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology—0
Meta Operator for Complex Query Answering on Knowledge Graphs—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on 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