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 2651–2700 of 3569 papers

TitleStatusHype
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
Domain-Free Adversarial Splitting for Domain Generalization—0
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations—0
Semantic Inference Network for Few-shot Streaming Label Learning—0
Navigating the Trade-Off between Learning Efficacy and Processing Efficiency in Deep Neural Networks—0
Few-Round Learning for Federated Learning—0
Robust Meta-learning with Noise via Eigen-Reptile—0
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory—0
Meta-Learned Confidence for Transductive Few-shot Learning—0
Meta Gradient Boosting Neural Networks—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Learning to Recover from Failures using Memory—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
Optimal allocation of data across training tasks in meta-learning—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
MM-FSOD: Meta and metric integrated few-shot object detection—0
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering—0
Meta Learning Backpropagation And Improving It—0
AutonoML: Towards an Integrated Framework for Autonomous Machine LearningCode0
MetaAugment: Sample-Aware Data Augmentation Policy Learning—0
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Universal Policies for Software-Defined MDPs—0
A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations—0
Few-shot Sequence Learning with Transformers—0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Learning active learning at the crossroads? evaluation and discussion—0
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
Variable-Shot Adaptation for Online Meta-Learning—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—0
Adaptive Submodular Meta-Learning—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior KnowledgeCode0
Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning—0
Cold-start Sequential Recommendation via Meta Learner—0
Portrait Neural Radiance Fields from a Single Image—0
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting—0
Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test—0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learningCode0
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-AlignmentCode0
Forecast with Forecasts: Diversity Matters—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