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 501–510 of 3073 papers

Title	Date	Tasks	Status	Score
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available	5
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning	Mar 13, 2024	Active LearningChange Point Detection	CodeCode Available	5
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available	5
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	5
Active Classification with Uncertainty Comparison Queries	Aug 3, 2020	Active LearningClassification	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
Generative Active Learning for Image Synthesis Personalization	Mar 22, 2024	Active LearningImage Generation	CodeCode Available	5
Generative Adversarial Active Learning for Unsupervised Outlier Detection	Sep 28, 2018	Active LearningBinary Classification	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5

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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