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 1291–1300 of 3073 papers

Title	Date	Tasks	Status
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data	Apr 12, 2016	Active Learning	—Unverified
Deep Deterministic Uncertainty: A New Simple Baseline	Jan 1, 2023	Active LearningSemantic Segmentation	—Unverified
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper	Feb 2, 2017	Active LearningGPU	—Unverified
Adaptive Active Hypothesis Testing under Limited Information	Dec 1, 2017	Active LearningTwo-sample testing	—Unverified
Detecting Repeating Objects using Patch Correlation Analysis	Apr 11, 2019	Active LearningObject	—Unverified
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles	May 1, 2019	Active LearningGeneral Classification	—Unverified
Deterministic Langevin Unconstrained Optimization with Normalizing Flows	Oct 1, 2023	Active LearningBayesian Optimization	—Unverified
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles	Nov 9, 2018	Active LearningGeneral Classification	—Unverified
Deep imitation learning for molecular inverse problems	Dec 1, 2019	Active LearningGraph Generation	—Unverified
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout	Jun 23, 2019	Active LearningBayesian Inference	—Unverified

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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