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

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Showing 2451–2500 of 3569 papers

TitleStatusHype
Learning where to learn—0
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications—0
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification—0
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
Learn to Adapt for Monocular Depth Estimation—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
Learn To Learn More Precisely—0
Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks—0
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning—0
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Leveraging Language for Accelerated Learning of Tool Manipulation—0
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News—0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios—0
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data—0
Leveraging The Topological Consistencies of Learning in Deep Neural Networks—0
Lifelong Word Embedding via Meta-Learning—0
LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding—0
LiMAML: Personalization of Deep Recommender Models via Meta Learning—0
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning—0
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior—0
Local transfer learning from one data space to another—0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
Long Short-Term Temporal Meta-learning in Online Recommendation—0
Looking back to lower-level information in few-shot learning—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Loss meta-learning for forecasting—0
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing—0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
M2I2: Learning Efficient Multi-Agent Communication via Masked State Modeling and Intention Inference—0
MAC: A Meta-Learning Approach for Feature Learning and Recombination—0
Machine Learning: a Lecture Note—0
Machine Learning Approaches For Motor Learning: A Short Review—0
Machine Theory of Mind—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy—0
Making Graph Neural Networks Worth It for Low-Data Molecular Machine 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