SOTAVerified|Agents Browse Leaderboard About Blog

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 3011–3020 of 3073 papers

Title	Date	Tasks	Status
Gaussian Switch Sampling: A Second Order Approach to Active Learning	Feb 16, 2023	Active LearningInformativeness	CodeCode Available
Machine learning of hierarchical clustering to segment 2D and 3D images	Mar 25, 2013	Active LearningBIG-bench Machine Learning	CodeCode Available
Automated Performance Testing Based on Active Deep Learning	Apr 5, 2021	Active LearningDeep Learning	CodeCode Available
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition	May 31, 2019	Active LearningActivity Recognition	CodeCode Available
Targeted active learning for probabilistic models	Oct 21, 2022	Active Learning	CodeCode Available
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling	Jul 21, 2019	Active LearningOrgan Segmentation	CodeCode Available
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles	May 2, 2023	Active LearningDomain Generalization	CodeCode Available
Algorithm Selection for Deep Active Learning with Imbalanced Datasets	Feb 14, 2023	Active Learning	CodeCode Available
Single Shot Active Learning using Pseudo Annotators	May 17, 2018	Active Learning	CodeCode Available
Active Learning with Partial Feedback	Feb 21, 2018	Active Learning	CodeCode Available

Show:10 25 50

← PrevPage 302 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