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 2801–2810 of 3073 papers

Title	Date	Tasks	Status
Efficient Test Collection Construction via Active Learning	Jan 17, 2018	Active Learning	CodeCode Available
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation	Nov 18, 2024	Active Learningcounterfactual	CodeCode Available
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition	Jun 21, 2022	Active LearningClustering	CodeCode Available
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available
Selection via Proxy: Efficient Data Selection for Deep Learning	Jun 26, 2019	Active LearningComputational Efficiency	CodeCode Available
Active Learning to Guide Labeling Efforts for Question Difficulty Estimation	Sep 14, 2024	Active LearningQuestion Answering	CodeCode Available
Unsupervised Pool-Based Active Learning for Linear Regression	Jan 14, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available
Continual Developmental Neurosimulation Using Embodied Computational Agents	Mar 7, 2021	Active LearningContinual Learning	CodeCode Available
An active learning convolutional neural network for predicting river flow in a human impacted system	Oct 17, 2023	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