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 1071–1080 of 3073 papers

TitleStatusHype
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Plug and Play Active Learning for Object DetectionCode1
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions—0
Finding active galactic nuclei through FinkCode1
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors—0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning with Expected Error Reduction—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