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 1376–1400 of 3569 papers

TitleStatusHype
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
Federated Conditional Stochastic Optimization—0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples—0
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems—0
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning—0
Semi-Supervised Variational Inference over Nonlinear Channels—0
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration—0
A Systematic Review of Few-Shot Learning in Medical Imaging—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
MAD: Meta Adversarial Defense BenchmarkCode0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
Convergence of Gradient-based MAML in LQR—0
A supervised generative optimization approach for tabular data—0
Retrieval-Augmented Meta Learning for Low-Resource Text Classification—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Amortised Inference in Bayesian Neural NetworksCode0
Towards General and Efficient Online Tuning for Spark—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