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 791–800 of 3073 papers

Title	Date	Tasks	Status
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks	Feb 2, 2023	Active LearningDecision Making	—Unverified
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images	Jul 3, 2019	Active LearningCollaborative Filtering	—Unverified
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks	Feb 19, 2022	Active LearningPrediction	—Unverified
A Simple Approximation Algorithm for Optimal Decision Tree	May 21, 2025	Active LearningMedical Diagnosis	—Unverified
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking	Jul 27, 2020	Active LearningVideo Captioning	—Unverified
ALdataset: a benchmark for pool-based active learning	Oct 16, 2020	Active LearningBenchmarking	—Unverified
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches	May 18, 2016	Active Learning	—Unverified
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment	Oct 17, 2024	Active LearningContrastive Learning	—Unverified
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification	Sep 30, 2020	Active LearningData Augmentation	—Unverified
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images	Mar 9, 2023	Active LearningDiagnostic	—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