SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2376–2400 of 3073 papers

TitleStatusHype
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels—0
Making RL with Preference-based Feedback Efficient via Randomization—0
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs—0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models—0
Mapping oil palm density at country scale: An active learning approach—0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
Markerless Multi-view 3D Human Pose Estimation: a survey—0
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning—0
MAViC: Multimodal Active Learning for Video Captioning—0
Maximally Separated Active Learning—0
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies—0
A Unified Batch Selection Policy for Active Metric Learning—0
MEAL: Manifold Embedding-based Active Learning—0
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection—0
Mean-Variance Analysis in Bayesian Optimization under Uncertainty—0
MEAOD: Model Extraction Attack against Object Detectors—0
Measuring Mother-Infant Emotions By Audio Sensing—0
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials—0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis—0
MedCAT -- Medical Concept Annotation Tool—0
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation—0
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions—0
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified