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 2591–2600 of 3073 papers

TitleStatusHype
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning—0
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation—0
Deep Active Learning for Anomaly Detection—0
Distribution Aware Active Learning—0
Teacher's Perception in the Classroom—0
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification—0
Single Shot Active Learning using Pseudo AnnotatorsCode0
Progress & Compress: A scalable framework for continual learningCode0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—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