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 371380 of 3073 papers

TitleStatusHype
The Search for Squawk: Agile Modeling in Bioacoustics0
Reduced-order structure-property linkages for stochastic metamaterials0
TActiLE: Tiny Active LEarning for wearable devices0
Inconsistency-based Active Learning for LiDAR Object Detection0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
Geometry-aware Active Learning of Spatiotemporal Dynamic 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