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 12611270 of 3073 papers

TitleStatusHype
Deep Active Learning for Text Classification with Diverse Interpretations0
Deep Active Learning for Video-based Person Re-identification0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion0
Deep Active Learning in the Presence of Label Noise: A Survey0
Deep Active Learning over the Long Tail0
Adaptive Active Learning for Image Classification0
Deep Active Learning Using Barlow Twins0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation0
Active Learning for Nonlinear System Identification with Guarantees0
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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