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 18911900 of 3073 papers

TitleStatusHype
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers0
Double-Barreled Question Detection at Momentive0
Downstream-Pretext Domain Knowledge Traceback for Active Learning0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy0
DroidStar: Callback Typestates for Android Classes0
Dropout-based Active Learning for Regression0
Dual Active Learning for Reinforcement Learning from Human Feedback0
Dual Adversarial Network for Deep Active Learning0
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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