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 19111920 of 3073 papers

TitleStatusHype
Similarity Search for Efficient Active Learning and Search of Rare Concepts0
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning0
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization0
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains0
Single Image Object Counting and Localizing using Active-Learning0
Single-Modal Entropy based Active Learning for Visual Question Answering0
Small-GAN: Speeding Up GAN Training Using Core-sets0
Small-Text: Active Learning for Text Classification in Python0
Smart Active Sampling to enhance Quality Assurance Efficiency0
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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