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 1801–1810 of 3073 papers

TitleStatusHype
Refined Mechanism Design for Approximately Structured Priors via Active Regression—0
regAL: Python Package for Active Learning of Regression Problems—0
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
Region-level Active Detector Learning—0
Reinforced Meta Active Learning—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Reinforcement Learning from Human Feedback with Active Queries—0
Relevance feedback strategies for recall-oriented neural information retrieval—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