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 1231–1240 of 3073 papers

Title	Date	Tasks	Status	Hype
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method	Jul 8, 2022	Active LearningBayesian Optimization	—Unverified	0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems	Jul 8, 2022	Active LearningAnomaly Detection	—Unverified	0
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering	Jul 7, 2022	Active LearningConversational Question Answering	—Unverified	0
Mitigating shortage of labeled data using clustering-based active learning with diversity exploration	Jul 6, 2022	Active LearningClustering	CodeCode Available	0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning	Jul 5, 2022	Active Learningimage-classification	—Unverified	0
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios	Jul 4, 2022	Active Learning	—Unverified	0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis	Jul 3, 2022	Active LearningDescriptive	—Unverified	0
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation	Jul 2, 2022	Active LearningBenchmarking	CodeCode Available	1
Teaching Interactively to Learn Emotions in Natural Language	Jul 1, 2022	Active Learning	—Unverified	0
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jul 1, 2022	Active Learningtext-classification	CodeCode Available	1

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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