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

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Showing 531–540 of 3073 papers

TitleStatusHype
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
An information-matching approach to optimal experimental design and active learning—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal SystemsCode0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration—0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry—0
DISCERN: Decoding Systematic Errors in Natural Language for Text ClassifiersCode0
Active Learning for Vision-Language Models—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified