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 1121–1130 of 3073 papers

TitleStatusHype
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
A critical look at the current train/test split in machine learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Auto-Differentiating Linear Algebra—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—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