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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 841–850 of 3073 papers

Title	Date	Tasks	Status	Score
Partition-Based Active Learning for Graph Neural Networks	Jan 23, 2022	Active LearningNode Classification	CodeCode Available	5
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Jun 19, 2019	Active Learning	CodeCode Available	5
Batch Decorrelation for Active Metric Learning	May 20, 2020	Active LearningDiversity	CodeCode Available	5
Batch Active Learning Using Determinantal Point Processes	Jun 19, 2019	Active LearningDiversity	CodeCode Available	5
Active learning via informed search in movement parameter space for efficient robot task learning and transfer	Feb 21, 2019	Active Learning	CodeCode Available	5
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification	Jun 29, 2023	Active LearningClassification	CodeCode Available	5
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals	May 16, 2014	Active Learning	CodeCode Available	5
Batch Active Preference-Based Learning of Reward Functions	Oct 10, 2018	Active Learning	CodeCode Available	5
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications	May 5, 2022	Active Learning	CodeCode Available	5
BatchGFN: Generative Flow Networks for Batch Active Learning	Jun 26, 2023	Active Learning	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