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 2651–2660 of 3073 papers

TitleStatusHype
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification—0
Fast kNN mode seeking clustering applied to active learning—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Noisy Natural Gradient as Variational InferenceCode0
Active Learning from Peers—0
Adaptive Active Hypothesis Testing under Limited Information—0
Active Regression via Linear-Sample Sparsification—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Bayesian Active Edge Evaluation on Expensive Graphs—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