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 3026–3050 of 3569 papers

TitleStatusHype
HyperLoRA for PDEs—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks—0
HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks—0
Hypernymy Detection for Low-Resource Languages via Meta Learning—0
Hyperparameter Optimization in Machine Learning—0
HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning—0
ICL Markup: Structuring In-Context Learning using Soft-Token Tags—0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
Identifying Physical Law of Hamiltonian Systems via Meta-Learning—0
Image Retrieval And Classification Using Local Feature Vectors—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved Few-Shot Visual Classification—0
Improved Generalization Risk Bounds for Meta-Learning with PAC-Bayes-kl Analysis—0
Improved Meta Learning for Low Resource Speech Recognition—0
Improved Meta-Learning Training for Speaker Verification—0
Improved Relation Networks for End-to-End Speaker Verification and Identification—0
Improve Noise Tolerance of Robust Loss via Noise-Awareness—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—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