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 351–375 of 3073 papers

TitleStatusHype
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Active Learning for Efficient Testing of Student Programs—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
Action State Update Approach to Dialogue Management—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
A Benchmark and Comparison of Active Learning for Logistic Regression—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
ActDroid: An active learning framework for Android malware detection—0
Active learning for distributionally robust level-set estimation—0
Active Learning for Direct Preference Optimization—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active learning with biased non-response to label requests—0
Active learning for detection of stance components—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning for WBAN-based Health Monitoring—0
Active Learning for Dependency Parsing with Partial Annotation—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Active Learning with a Drifting Distribution—0
Active Learning with Combinatorial Coverage—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Label Comparisons—0
Active Learning for Delineation of Curvilinear Structures—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