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 1861–1870 of 3073 papers

TitleStatusHype
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Sampling Approach Matters: Active Learning for Robotic Language Acquisition—0
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature—0
Sampling from a k-DPP without looking at all items—0
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data—0
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions—0
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data—0
Scalable Active Learning for Object Detection—0
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data—0
Scaling Evidence-based Instructional Design Expertise through Large Language Models—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