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 1651–1660 of 3073 papers

TitleStatusHype
ALLWAS: Active Learning on Language models in WASserstein space—0
Active learning for imbalanced data under cold start—0
The Application of Active Query K-Means in Text Classification—0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users—0
Zero-Round Active Learning—0
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning—0
Revisiting Uncertainty-based Query Strategies for Active Learning with TransformersCode1
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
RISAN: Robust Instance Specific Abstention NetworkCode0
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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