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
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models	Mar 2, 2024	Active LearningFew-Shot Learning	CodeCode Available
Deep Active Learning for Anchor User Prediction	Jun 18, 2019	Active LearningPrediction	CodeCode Available
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available
Textual Membership Queries	May 11, 2018	Active LearningGeneral Classification	CodeCode Available
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes	Nov 9, 2020	Active Learning	CodeCode Available
Image-based Detection of Surface Defects in Concrete during Construction	Aug 3, 2022	Active LearningDefect Detection	CodeCode Available
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions	Mar 29, 2017	Active LearningBayesian Inference	CodeCode Available
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available
State-Relabeling Adversarial Active Learning	Apr 10, 2020	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