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 25712580 of 3073 papers

TitleStatusHype
Active learning for deep semantic parsing0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
Probabilistic Bisection with Spatial Metamodels0
Sampling and Reconstruction of Signals on Product GraphsCode0
Autonomous Wireless Systems with Artificial Intelligence0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection0
Dropout-based Active Learning for Regression0
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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