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 16911700 of 3073 papers

TitleStatusHype
Partial-Adaptive Submodular Maximization0
Partial-Monotone Adaptive Submodular Maximization0
Participation in TREC 2020 COVID Track Using Continuous Active Learning0
Parting with Illusions about Deep Active Learning0
Partitioned Active Learning for Heterogeneous Systems0
Passive and Active Learning of Driver Behavior from Electric Vehicles0
Patient Aware Active Learning for Fine-Grained OCT Classification0
Payoff Information and Learning in Signaling Games0
Peer to Peer Learning Platform Optimized With Machine Learning0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving0
Show:102550
← PrevPage 170 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