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 1201–1210 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores	Aug 10, 2022	Active Learningglobal-optimization	—Unverified	0
Multi-task Active Learning for Pre-trained Transformer-based Models	Aug 10, 2022	Active LearningMulti-Task Learning	CodeCode Available	0
Active Learning for Non-Parametric Choice Models	Aug 5, 2022	Active Learning	—Unverified	0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data	Aug 5, 2022	Active LearningLearning-To-Rank	—Unverified	0
A Holistic Approach to Undesired Content Detection in the Real World	Aug 5, 2022	Active Learning	CodeCode Available	1
Deep Surrogate of Modular Multi Pump using Active Learning	Aug 4, 2022	Active Learning	—Unverified	0
Image-based Detection of Surface Defects in Concrete during Construction	Aug 3, 2022	Active LearningDefect Detection	CodeCode Available	0
Active Learning on a Programmable Photonic Quantum Processor	Aug 3, 2022	Active LearningBIG-bench Machine Learning	—Unverified	0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning	Aug 2, 2022	Active LearningFew-Shot Image Classification	CodeCode Available	0
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation	Aug 2, 2022	Active LearningSegmentation	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