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 1141–1150 of 3073 papers

TitleStatusHype
OpenAL: Evaluation and Interpretation of Active Learning StrategiesCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Towards Active Learning for Action Spotting in Association Football Videos—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection—0
Fairness-Aware Data Valuation for Supervised Learning—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