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 1241–1250 of 3073 papers

TitleStatusHype
Data-Efficient Learning via Minimizing Hyperspherical Energy—0
Black-box Generalization of Machine Teaching—0
Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities—0
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels—0
Mitigating sampling bias in risk-based active learning via an EM algorithm—0
Deep Active Learning for Regression Using ε-weighted Hybrid Query StrategyCode1
Cost-Sensitive Active Learning for Incomplete DataCode0
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers—0
Human-in-the-Loop Large-Scale Predictive Maintenance of WorkstationsCode2
Patient Aware Active Learning for Fine-Grained OCT Classification—0
Show:102550
← PrevPage 125 of 308Next →

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