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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 771–780 of 3073 papers

Title	Date	Tasks	Status	Score
Noisy Batch Active Learning with Deterministic Annealing	Sep 27, 2019	Active LearningClustering	CodeCode Available	5
Learning Linear Dynamical Systems with Semi-Parametric Least Squares	Feb 2, 2019	Active Learning	CodeCode Available	5
Active Learning of Spin Network Models	Mar 25, 2019	Active Learning	CodeCode Available	5
Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling	Mar 3, 2025	Active LearningDecision Making	CodeCode Available	5
Benchmarking of Query Strategies: Towards Future Deep Active Learning	Dec 10, 2023	Active LearningBenchmarking	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Active Keyword Selection to Track Evolving Topics on Twitter	Sep 22, 2022	Active Learning	CodeCode Available	5
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks	May 29, 2025	Active Learning	CodeCode Available	5
Active Learning for Neural Machine Translation	Dec 30, 2022	Active LearningMachine Translation	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