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 1201–1250 of 3569 papers

TitleStatusHype
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
Customized Conversational Recommender Systems—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Curriculum Meta-Learning for Next POI Recommendation—0
A statistical physics framework for optimal learning—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
Improved Few-Shot Visual Classification—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
cross-modal knowledge enhancement mechanism for few-shot learning—0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
Identifying Physical Law of Hamiltonian Systems via Meta-Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Cross-Lingual Transfer with MAML on Trees—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
A Single-Timescale Method for Stochastic Bilevel Optimization—0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
Cross-heterogeneity Graph Few-shot Learning—0
Cross-Frequency Time Series Meta-Forecasting—0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning—0
A Simple Recipe to Meta-Learn Forward and Backward Transfer—0
A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
ACE: Adapting to Changing Environments for Semantic Segmentation—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
ICL Markup: Structuring In-Context Learning using Soft-Token Tags—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Hyperparameter Optimization in Machine Learning—0
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—0
CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks—0
A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems—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