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 26512660 of 3073 papers

TitleStatusHype
A Pipeline for Post-Crisis Twitter Data Acquisition0
Localization-Aware Active Learning for Object Detection0
Active Community Detection with Maximal Expected Model Change0
Semi-automated Annotation of Signal Events in Clinical EEG Data0
Few-Shot Learning with Graph Neural NetworksCode0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification0
Fast kNN mode seeking clustering applied to active learning0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields0
Noisy Natural Gradient as Variational InferenceCode0
Active Learning from Peers0
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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