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 2561–2570 of 3073 papers

TitleStatusHype
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling—0
Making Efficient Use of a Domain Expert's Time in Relation Extraction—0
Practical Obstacles to Deploying Active Learning—0
Evaluating Active Learning Heuristics for Sequential Diagnosis—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Towards more Reliable Transfer Learning—0
Distilling the Posterior in Bayesian Neural Networks—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Cost-Sensitive Active Learning for Dialogue State Tracking—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