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 1251–1275 of 3569 papers

TitleStatusHype
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning—0
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter—0
Correction Networks: Meta-Learning for Zero-Shot Learning—0
Agnostic Sharpness-Aware Minimization—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations—0
ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning—0
Convolutional Neural Processes for Inpainting Satellite Images—0
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence—0
Improving both domain robustness and domain adaptability in machine translation—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning—0
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning—0
Interpretable Meta-Learning of Physical Systems—0
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation—0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
Global Perception Based Autoregressive Neural Processes—0
When Does MAML Objective Have Benign Landscape?—0
Convergence Properties of Stochastic Hypergradients—0
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters—0
Show:102550
← PrevPage 51 of 143Next →
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