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 1476–1500 of 3569 papers

TitleStatusHype
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations—0
Black-box Prompt Tuning with Subspace Learning—0
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks—0
A Meta Understanding of Meta-Learning—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—0
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Generalization of First-Order Meta-Learning—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
BI-MAML: Balanced Incremental Approach for Meta Learning—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
Enhanced Bilevel Optimization via Bregman Distance—0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Bilevel Programming for Hyperparameter Optimization and Meta-Learning—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
Bilevel Optimization for Machine Learning: Algorithm Design and Convergence Analysis—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
Bi-Level Meta-Learning for Few-Shot Domain Generalization—0
A Meta-Reinforcement Learning Approach to Process Control—0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles—0
Bilevel Continual 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