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 426–450 of 3073 papers

TitleStatusHype
Active learning with biased non-response to label requests—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Expert Advice—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Learning with Importance Sampling—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning with Logged Data—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Active Learning with Multiple Kernels—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Active Learning with Oracle Epiphany—0
Active Learning for Delineation of Curvilinear Structures—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Active Learning Polynomial Threshold Functions—0
Active Learning with Rationales for Text Classification—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—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