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 1581–1590 of 3073 papers

Title	Date	Tasks	Status
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning	Dec 1, 2015	Active LearningBIG-bench Machine Learning	—Unverified
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels	Jun 1, 2013	Active LearningRelation Extraction	—Unverified
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition	Jun 20, 2014	Active Learning	—Unverified
Noise-tolerant, Reliable Active Classification with Comparison Queries	Jan 15, 2020	Active LearningClassification	—Unverified
Noisy Generalized Binary Search	Dec 1, 2009	Active LearningScheduling	—Unverified
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity	Jul 1, 2021	Active LearningBIG-bench Machine Learning	—Unverified
Nonparametric active learning for cost-sensitive classification	Sep 30, 2023	Active LearningClassification	—Unverified
Nonparametric adaptive active learning under local smoothness condition	Feb 22, 2021	Active Learningvalid	—Unverified
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning	Oct 11, 2019	Active LearningAll	—Unverified
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage	Jun 7, 2023	Active LearningClustering	—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