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 10811090 of 3073 papers

TitleStatusHype
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing0
Active Learning with Expected Error Reduction0
Addressing the Item Cold-start Problem by Attribute-driven Active Learning0
Addressing practical challenges in Active Learning via a hybrid query strategy0
Active Learning for Post-Editing Based Incrementally Retrained MT0
Addressing Limited Data for Textual Entailment Across Domains0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning0
Active Learning Algorithms for Graphical Model Selection0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS0
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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