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 1751–1760 of 3073 papers

TitleStatusHype
Small-Text: Active Learning for Text Classification in Python—0
Offline Preference-Based Apprenticeship Learning—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
The Application of Active Query K-Means in Text Classification—0
Active learning for imbalanced data under cold start—0
Zero-Round Active Learning—0
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—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