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 2551–2560 of 3073 papers

Title	Date	Tasks	Status
Sprucing up the trees -- Error detection in treebanks	Aug 1, 2018	Active LearningBayesian Inference	—Unverified
Active Learning for Interactive Neural Machine Translation of Data Streams	Jul 30, 2018	Active LearningMachine Translation	—Unverified
Leveraging Motion Priors in Videos for Improving Human Segmentation	Jul 30, 2018	Active LearningDomain Adaptation	—Unverified
Noise Contrastive Priors for Functional Uncertainty	Jul 24, 2018	Active Learning	CodeCode Available
A Structured Perspective of Volumes on Active Learning	Jul 24, 2018	Active Learning	—Unverified
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification	Jul 22, 2018	Active LearningClassification	CodeCode Available
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time	Jul 20, 2018	Active LearningGeneral Classification	—Unverified
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS	Jul 18, 2018	Active Learning	—Unverified
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy	Jul 18, 2018	Active LearningMedical Image Analysis	—Unverified
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach	Jul 18, 2018	Active LearningBIG-bench Machine Learning	CodeCode Available

Show:10 25 50

← PrevPage 256 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