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 10111020 of 3073 papers

TitleStatusHype
Computational Assessment of Hyperpartisanship in News TitlesCode0
Scalable Batch Acquisition for Deep Bayesian Active LearningCode0
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data0
Combining Self-labeling with Selective Sampling0
A domain-decomposed VAE method for Bayesian inverse problems0
Active Learning for Abstractive Text SummarizationCode0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Active Learning Guided by Efficient Surrogate Learners0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides0
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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