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 701–710 of 3073 papers

Title	Date	Tasks	Status	Score
Fairness Without Harm: An Influence-Guided Active Sampling Approach	Feb 20, 2024	Active LearningAttribute	CodeCode Available	5
Falcon: Fair Active Learning using Multi-armed Bandits	Jan 23, 2024	Active LearningAttribute	CodeCode Available	5
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions	Mar 29, 2017	Active LearningBayesian Inference	CodeCode Available	5
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise	Jul 8, 2024	Active LearningClassification	CodeCode Available	5
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available	5
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?	Nov 6, 2020	Active LearningBenchmarking	CodeCode Available	5
Buy Me That Look: An Approach for Recommending Similar Fashion Products	Aug 26, 2020	Active LearningArticles	CodeCode Available	5
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning	Jan 31, 2023	Active LearningComputational Efficiency	CodeCode Available	5
Few-Shot Learning with Graph Neural Networks	Nov 10, 2017	Active LearningCross-Domain Few-Shot	CodeCode Available	5
Adapting Coreference Resolution Models through Active Learning	Apr 15, 2021	Active LearningClustering	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