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

Showing 16011610 of 3073 papers

TitleStatusHype
THMA: Tencent HD Map AI System for Creating HD Map Annotations0
Thompson Sampling for Dynamic Pricing0
Thompson sampling for improved exploration in GFlowNets0
Ticket-BERT: Labeling Incident Management Tickets with Language Models0
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning0
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation0
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning0
Towards Active Learning Based Smart Assistant for Manufacturing0
Towards Active Learning for Action Spotting in Association Football Videos0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified