SOTAVerified|Agents Browse Leaderboard About

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 891–900 of 3073 papers

Title	Date	Tasks	Status	Score
Batch Decorrelation for Active Metric Learning	May 20, 2020	Active LearningDiversity	CodeCode Available	5
Bayesian active learning for optimization and uncertainty quantification in protein docking	Jan 31, 2019	Active LearningBinary Classification	CodeCode Available	5
Batch Active Learning at Scale	Jul 29, 2021	Active LearningDiversity	CodeCode Available	5
Batch Active Learning Using Determinantal Point Processes	Jun 19, 2019	Active LearningDiversity	CodeCode Available	5
Active Gradual Machine Learning for Entity Resolution	Jan 16, 2022	Active LearningBIG-bench Machine Learning	CodeCode Available	5
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation	Oct 12, 2023	Active LearningLIDAR Semantic Segmentation	CodeCode Available	5
Batch Active Preference-Based Learning of Reward Functions	Oct 10, 2018	Active Learning	CodeCode Available	5
CFlowNets: Continuous Control with Generative Flow Networks	Mar 4, 2023	Active Learningcontinuous-control	CodeCode Available	5
Cost-Sensitive Active Learning for Incomplete Data	Jun 23, 2022	Active LearningAttribute	CodeCode Available	5
Efficient Concept Drift Handling for Batch Android Malware Detection Models	Sep 18, 2023	Active LearningAndroid Malware Detection	CodeCode Available	5

Show:10 25 50

← PrevPage 90 of 308Next →

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