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 2581–2590 of 3073 papers

Title	Date	Tasks	Status
Probabilistic Model-Agnostic Meta-Learning	Jun 7, 2018	Active LearningFew-Shot Image Classification	—Unverified
Model-based active learning to detect isometric deformable objects in the wild with deep architectures	Jun 7, 2018	Active LearningObject	—Unverified
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data	Jun 7, 2018	Active LearningGeneral Classification	—Unverified
Finding Convincing Arguments Using Scalable Bayesian Preference Learning	Jun 6, 2018	Active LearningVariational Inference	CodeCode Available
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus	Jun 5, 2018	Active LearningMulti-Task Learning	—Unverified
The Power of Ensembles for Active Learning in Image Classification	Jun 1, 2018	Active LearningGeneral Classification	—Unverified
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]	Jun 1, 2018	Active LearningAttribute	CodeCode Available
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

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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