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 2591–2600 of 3073 papers

Title	Date	Tasks	Status
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus	Jun 5, 2018	Active LearningMulti-Task Learning	—Unverified
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]	Jun 1, 2018	Active LearningAttribute	CodeCode Available
The Power of Ensembles for Active Learning in Image Classification	Jun 1, 2018	Active LearningGeneral Classification	—Unverified
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning	May 31, 2018	Active LearningBoundary Detection	—Unverified
Active and Adaptive Sequential learning	May 29, 2018	Active Learning	—Unverified
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization	May 28, 2018	Active LearningContinuous Object Recognition	CodeCode Available
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks	May 28, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Deep Active Learning for Anomaly Detection	May 23, 2018	Active LearningAnomaly Detection	—Unverified
Distribution Aware Active Learning	May 23, 2018	Active Learning	—Unverified
Addressing the Item Cold-start Problem by Attribute-driven Active Learning	May 23, 2018	Active LearningAttribute	—Unverified

Show:10 25 50

← PrevPage 260 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