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 26812690 of 3073 papers

TitleStatusHype
libact: Pool-based Active Learning in PythonCode0
Personalized Image Aesthetics0
Active Learning amidst Logical KnowledgeCode0
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search0
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions0
Anomaly Detection in Hierarchical Data Streams under Unknown Models0
Structured Prediction via Learning to Search under Bandit Feedback0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector0
An Eye-tracking Study of Named Entity Annotation0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts0
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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