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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1211–1220 of 3073 papers

TitleStatusHype
Data-driven discovery of free-form governing differential equations—0
Data-driven surrogate modelling and benchmarking for process equipment—0
Deep active learning for nonlinear system identification—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Deep Active Learning in the Open World—0
Data-Efficient Learning via Minimizing Hyperspherical Energy—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
A Finite-Horizon Approach to Active Level Set Estimation—0
Data-efficient Online Classification with Siamese Networks and Active Learning—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—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