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 2611–2620 of 3073 papers

TitleStatusHype
Bayesian active learning for choice models with deep Gaussian processes—0
modAL: A modular active learning framework for PythonCode0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—0
Active Learning for Breast Cancer Identification—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Active Learning for Efficient Testing of Student Programs—0
Derivative free optimization via repeated classificationCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Active covariance estimation by random sub-sampling of variables—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