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 1826–1850 of 3569 papers

TitleStatusHype
Rethinking Meta-Learning from a Learning Lens—0
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning—0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Retrieval-Augmented Meta Learning for Low-Resource Text Classification—0
Revealing the Incentive to Cause Distributional Shift—0
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship—0
Revisiting Meta-Learning as Supervised Learning—0
Reward Shaping via Meta-Learning—0
Riemannian Geometric-based Meta Learning—0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
ROAM: Recurrently Optimizing Tracking Model—0
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation—0
Robustifying Sequential Neural Processes—0
Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning—0
Robust Meta Learning for Image based tasks—0
Robust Meta-learning for Mixed Linear Regression with Small Batches—0
Robust Meta-learning with Noise via Eigen-Reptile—0
Role of two learning rates in convergence of model-agnostic meta-learning—0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
s-Adaptive Decoupled Prototype for Few-Shot Object Detection—0
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework—0
Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception—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