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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 861–870 of 3073 papers

Title	Date	Tasks	Status
A Contextual Bandit Approach for Stream-Based Active Learning	Jan 24, 2017	Active LearningDecision Making	—Unverified
Automated Discovery of Pairwise Interactions from Unstructured Data	Sep 11, 2024	Active Learning	—Unverified
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection	Jul 8, 2021	Active LearningDeep Reinforcement Learning	—Unverified
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields	Dec 15, 2017	Active LearningClassification	—Unverified
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization	Aug 31, 2022	Action LocalizationActive Learning	—Unverified
Automated Neural Patent Landscaping in the Small Data Regime	Jul 10, 2024	Active Learning	—Unverified
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy	Jun 1, 2014	Active LearningGeneral Classification	—Unverified
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment	Mar 3, 2025	Active LearningSegmentation	—Unverified
Active Learning with Expert Advice	Sep 26, 2013	Active Learning	—Unverified
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning	Sep 18, 2024	Active Learning	—Unverified

Show:10 25 50

← PrevPage 87 of 308Next →

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified