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 2541–2550 of 3073 papers

TitleStatusHype
Personalized Text Retrieval for Learners of Chinese as a Foreign Language—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
Sprucing up the trees -- Error detection in treebanks—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
A Structured Perspective of Volumes on Active Learning—0
Noise Contrastive Priors for Functional UncertaintyCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—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