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 2491–2500 of 3073 papers

Title	Date	Tasks	Status
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available
Double Q-PID algorithm for mobile robot control	Nov 1, 2018	Active LearningQ-Learning	CodeCode Available
Work Smart - Reducing Effort in Short-Answer Grading	Nov 1, 2018	Active LearningReading Comprehension	—Unverified
FrameIt: Ontology Discovery for Noisy User-Generated Text	Nov 1, 2018	Active LearningSemantic Role Labeling	—Unverified
APLenty: annotation tool for creating high-quality datasets using active and proactive learning	Nov 1, 2018	Active LearningMulti-Label Learning	—Unverified
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation	Oct 28, 2018	Active LearningBIG-bench Machine Learning	—Unverified
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation	Oct 23, 2018	Active LearningImage Segmentation	—Unverified
Prediction of Atomization Energy Using Graph Kernel and Active Learning	Oct 16, 2018	Active LearningManagement	—Unverified
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments	Oct 13, 2018	Action RecognitionActive Learning	—Unverified
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers	Oct 12, 2018	Active Learning	CodeCode Available

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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