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 2311–2320 of 3073 papers

TitleStatusHype
Gaussian Process Meta-Representations Of Neural Networks—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Sampling Bias in Deep Active Classification: An Empirical StudyCode0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active learning for level set estimation under cost-dependent input uncertainty—0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder—0
On weighted uncertainty sampling in active learning—0
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Learning to Sample: an Active Learning Framework—0
Active learning to optimise time-expensive algorithm selection—0
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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