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 28212830 of 3073 papers

TitleStatusHype
Automatic Playtesting for Game Parameter Tuning via Active Learning0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation0
AutoNLU: Detecting, root-causing, and fixing NLU model errors0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning0
Autonomous synthesis of metastable materials0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems0
AutoWS: Automated Weak Supervision Framework for Text Classification0
Show:102550
← PrevPage 283 of 308Next →

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