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 2276–2300 of 3073 papers

TitleStatusHype
Active Learning and CSI Acquisition for mmWave Initial Alignment—0
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances—0
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering—0
Active learning and negative evidence for language identification—0
Active Learning and Proofreading for Delineation of Curvilinear Structures—0
Active Learning and the Irish Treebank—0
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Learning Approach to Optimization of Experimental Control—0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning-based Model Predictive Coverage Control—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning by Querying Informative and Representative Examples—0
Active Learning Classification from a Signal Separation Perspective—0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—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