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

Showing 29412950 of 3073 papers

TitleStatusHype
Co-active Learning to Adapt Humanoid Movement for Manipulation0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR0
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning0
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents0
Cohort-Based Active Modality Acquisition0
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision0
Cold Start Active Learning Strategies in the Context of Imbalanced Classification0
Collaborative Active Learning in Conditional Trust Environment0
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Benchmark Results

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