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 12211230 of 3073 papers

TitleStatusHype
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
Active Pointly-Supervised Instance SegmentationCode1
Stream-based active learning with linear models0
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
Sample Efficient Learning of Predictors that Complement HumansCode0
Distributed Safe Learning and Planning for Multi-robot Systems0
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias0
Plex: Towards Reliability using Pretrained Large Model Extensions0
Uncertainty quantification for predictions of atomistic neural networksCode0
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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