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 30013010 of 3073 papers

TitleStatusHype
Towards a Tool for Interactive Concept Building for Large Scale Analysis in the Humanities0
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition0
SHEF-Lite: When Less is More for Translation Quality Estimation0
Online Active Learning for Cost Sensitive Domain Adaptation0
Annotating named entities in clinical text by combining pre-annotation and active learning0
Reducing Annotation Effort for Quality Estimation via Active Learning0
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction0
The Power of Localization for Efficiently Learning Linear Separators with Noise0
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy0
Auditing: Active Learning with Outcome-Dependent Query Costs0
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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