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 2741–2750 of 3073 papers

Title	Date	Tasks	Status
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study	Sep 10, 2024	Active LearningFederated Learning	—Unverified
Applied metamodelling for ATM performance simulations	Aug 7, 2023	Active LearningDecision Making	—Unverified
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data	Feb 24, 2025	Active LearningClassification	—Unverified
A Practical & Unified Notation for Information-Theoretic Quantities in ML	Jun 22, 2021	Active LearningExperimental Design	—Unverified
A Pre-trained Data Deduplication Model based on Active Learning	Jul 31, 2023	Active LearningData Augmentation	—Unverified
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks	Jun 14, 2023	Active LearningBackdoor Attack	—Unverified
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation	May 1, 2014	Active LearningMachine Translation	—Unverified
A quantum active learning algorithm for sampling against adversarial attacks	Dec 6, 2019	Active Learning	—Unverified
Are All Training Examples Created Equal? An Empirical Study	Nov 30, 2018	Active LearningAll	—Unverified
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes	Jan 25, 2021	Active LearningAutonomous Racing	—Unverified

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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