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 1371–1380 of 3073 papers

TitleStatusHype
Active Learning in Symbolic Regression with Physical Constraints—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Active Learning by Query by Committee with Robust Divergences—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
An Active Parameter Learning Approach to The Identification of Safe Regions—0
Active Learning in Recommendation Systems with Multi-level User Preferences—0
An active learning model to classify animal species in Hong Kong—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Show:102550
← PrevPage 138 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