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 2881–2890 of 3073 papers

Title	Date	Tasks	Status
BERT-PersNER: A New Model for Persian Named Entity Recognition	Sep 1, 2021	Active LearningMachine Translation	—Unverified
Best Arm Identification for Contaminated Bandits	Feb 26, 2018	Active Learning	—Unverified
Best Practices in Pool-based Active Learning for Image Classification	Sep 29, 2021	Active LearningBenchmarking	—Unverified
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control	Jan 1, 2021	Active Learning	—Unverified
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data	Sep 14, 2020	Active Learning	—Unverified
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification	Jun 2, 2023	Active Learning	—Unverified
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes	Jun 1, 2014	Active LearningAttribute	—Unverified
Beyond Disagreement-based Agnostic Active Learning	Jul 10, 2014	Active LearningGeneral Classification	—Unverified
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning	Jan 13, 2022	Active Learningcontinual few-shot learning	—Unverified
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection	Mar 10, 2023	3D Object DetectionActive Learning	—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