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 1611–1620 of 3073 papers

TitleStatusHype
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation—0
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Single Image Object Counting and Localizing using Active-Learning—0
Active Dialogue Simulation in Conversational Systems—0
Towards Computationally Feasible Deep Active Learning—0
Reducing the Long Tail Losses in Scientific Emulations with Active LearningCode0
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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