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 21712180 of 3073 papers

TitleStatusHype
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition0
Impact of Batch Size on Stopping Active Learning for Text Classification0
Impact of Stop Sets on Stopping Active Learning for Text Classification0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails0
Importance of Self-Consistency in Active Learning for Semantic Segmentation0
Improve Cost Efficiency of Active Learning over Noisy Dataset0
Improved Active Learning via Dependent Leverage Score Sampling0
Improved active output selection strategy for noisy environments0
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler0
Improved Algorithms for Agnostic Pool-based Active Classification0
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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