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 2621–2630 of 3073 papers

Title	Date	Tasks	Status
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning	Jun 9, 2025	Active LearningBenchmarking	CodeCode Available
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning	Oct 27, 2022	Active Learningimage-classification	CodeCode Available
Deep Bayesian Active Learning for Preference Modeling in Large Language Models	Jun 14, 2024	Active Learning	CodeCode Available
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models	Jun 3, 2023	Accented Speech RecognitionActive Learning	CodeCode Available
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications	May 5, 2022	Active Learning	CodeCode Available
Deep Bayesian Active Learning with Image Data	Mar 8, 2017	Active LearningDeep Learning	CodeCode Available
Deep Bayesian Active Semi-Supervised Learning	Mar 3, 2018	Active LearningData Augmentation	CodeCode Available
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	CodeCode Available
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning	Feb 21, 2024	Active Learningtext-classification	CodeCode Available
Deep Diffusion Processes for Active Learning of Hyperspectral Images	Jan 8, 2021	Active LearningDeep 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