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 971–980 of 3073 papers

TitleStatusHype
Online Active Learning For Sound Event Detection—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Evidential uncertainties on rich labels for active learning—0
Active Learning for Multilingual Fingerspelling Corpora—0
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening—0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Causal Discovery and Prediction: Methods and Algorithms—0
Active anomaly detection based on deep one-class classification—0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
Interactively Teaching an Inverse Reinforcement Learner with Limited FeedbackCode0
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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