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

Showing 101110 of 3569 papers

TitleStatusHype
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
Approximating Nash Equilibria in General-Sum Games via Meta-Learning0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings0
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving0
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining0
MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications0
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration0
Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life PredictionCode1
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified