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 1076–1100 of 3073 papers

TitleStatusHype
Active Learning for Dependency Parsing by A Committee of Parsers—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
ActDroid: An active learning framework for Android malware detection—0
A Benchmark and Comparison of Active Learning for Logistic Regression—0
Active Learning for WBAN-based Health Monitoring—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
A Word-and-Paradigm Workflow for Fieldwork Annotation—0
Active Learning with Oracle Epiphany—0
Active Learning for Delineation of Curvilinear Structures—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
Active Learning with Multiple Kernels—0
Active Learning for Deep Visual Tracking—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Autonomous synthesis of metastable materials—0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Active Learning with Logged Data—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Active Learning with Label Comparisons—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