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 2241–2250 of 3073 papers

Title	Date	Tasks	Status
Investigating Active Learning for Short-Answer Scoring	Jun 1, 2016	Active LearningReading Comprehension	—Unverified
Investigating Active Learning in Interactive Neural Machine Translation	Aug 1, 2021	Active LearningMachine Translation	—Unverified
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification	Jun 1, 2022	Active LearningExtreme Multi-Label Classification	—Unverified
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets	Apr 21, 2020	Active LearningWord Embeddings	—Unverified
ISEE.U: Distributed online active target localization with unpredictable targets	Oct 17, 2022	Active LearningCPU	—Unverified
Is margin all you need? An extensive empirical study of active learning on tabular data	Oct 7, 2022	Active LearningAll	—Unverified
Is More Data Better? Using Transformers-Based Active Learning for Efficient and Effective Detection of Abusive Language	Jan 16, 2022	Abusive LanguageActive Learning	—Unverified
Is there something I'm missing? Topic Modeling in eDiscovery	Jul 30, 2020	Active Learning	—Unverified
Investigating Active Learning and Meta-Learning for Iterative Peptide Design	Nov 20, 2019	Active LearningBinary Classification	—Unverified
Jasmine: A New Active Learning Approach to Combat Cybercrime	Aug 13, 2021	Active LearningIntrusion Detection	—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