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 2671–2680 of 3073 papers

Title	Date	Tasks	Status
Multitask Active Learning for Graph Anomaly Detection	Jan 24, 2024	Active LearningAnomaly Detection	CodeCode Available
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models	May 10, 2024	Active LearningExperimental Design	CodeCode Available
Sampling and Reconstruction of Signals on Product Graphs	Jun 30, 2018	Active LearningRecommendation Systems	CodeCode Available
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings	Aug 1, 2019	Active LearningDomain Adaptation	CodeCode Available
Incremental Robot Learning of New Objects with Fixed Update Time	May 17, 2016	Active LearningGeneral Classification	CodeCode Available
Multi-task Active Learning for Pre-trained Transformer-based Models	Aug 10, 2022	Active LearningMulti-Task Learning	CodeCode Available
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy	Oct 1, 2024	Active LearningPrivacy Preserving	CodeCode Available
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference	Dec 7, 2024	Active Learning	CodeCode Available
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification	Jul 31, 2023	Active LearningGraph Learning	CodeCode Available
Inferring solutions of differential equations using noisy multi-fidelity data	Jul 16, 2016	Active Learning	CodeCode Available

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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