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

Showing 12761300 of 3569 papers

TitleStatusHype
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill PrimitivesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal PredictionCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Interpretable Meta-Measure for Model PerformanceCode0
META-Learning Eligibility Traces for More Sample Efficient Temporal Difference LearningCode0
Gradient-Based Meta-Learning with Learned Layerwise Metric and SubspaceCode0
Active exploration in parameterized reinforcement learningCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Gradient Estimators for Implicit ModelsCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Meta-Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Meta Learning for Efficient Fine-Tuning of Large Language ModelsCode0
Few-Shot Adaptive Gaze EstimationCode0
An Investigation of Few-Shot Learning in Spoken Term ClassificationCode0
Classical Sequence Match is a Competitive Few-Shot One-Class LearnerCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh RecoveryCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified