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

Showing 21212130 of 3073 papers

TitleStatusHype
UPM system for WMT 20120
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring0
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review0
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified