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 26312640 of 3073 papers

TitleStatusHype
Best Arm Identification for Contaminated Bandits0
Active Learning with Logged Data0
Active Learning with Partial FeedbackCode0
Distributional Term Set Expansion0
Fast Interactive Image Retrieval using large-scale unlabeled data0
Thompson Sampling for Dynamic Pricing0
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings0
Greedy Active Learning Algorithm for Logistic Regression Models0
Improving Active Learning in Systematic Reviews0
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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