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 2581–2590 of 3073 papers

Title	Date	Tasks	Status
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jan 16, 2022	Active Learningtext-classification	—Unverified
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI	Jan 25, 2022	Active LearningDisaster Response	—Unverified
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization	Apr 21, 2021	Active LearningBayesian Optimization	—Unverified
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study	Jun 19, 2019	Active Learningreinforcement-learning	—Unverified
Adaptive Active Hypothesis Testing under Limited Information	Dec 1, 2017	Active LearningTwo-sample testing	—Unverified
Adaptive Active Learning for Image Classification	Jun 1, 2013	Active LearningClassification	—Unverified
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies	Apr 2, 2024	Active Learning	—Unverified
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation	Apr 3, 2023	Active LearningTransfer Learning	—Unverified
Importance sampling based active learning for parametric seismic fragility curve estimation	Sep 9, 2021	Active LearningExperimental Design	—Unverified
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference	May 24, 2021	Active LearningGPR	—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