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 2731–2740 of 3073 papers

Title	Date	Tasks	Status
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion	Apr 26, 2017	Active LearningClustering	—Unverified
Mining Object Parts from CNNs via Active Question-Answering	Apr 11, 2017	Active LearningObject	—Unverified
Active classification with comparison queries	Apr 11, 2017	Active LearningClassification	—Unverified
Parsimonious Random Vector Functional Link Network for Data Streams	Apr 10, 2017	Active Learning	—Unverified
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers	Apr 1, 2017	Active LearningDependency Parsing	—Unverified
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models	Apr 1, 2017	Active LearningKnowledge Graph Embeddings	—Unverified
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions	Mar 29, 2017	Active LearningBayesian Inference	CodeCode Available
Goal-Driven Dynamics Learning via Bayesian Optimization	Mar 27, 2017	Active LearningBayesian Optimization	—Unverified
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions	Mar 22, 2017	Active Learning	—Unverified
Episode-Based Active Learning with Bayesian Neural Networks	Mar 21, 2017	Active Learning	—Unverified

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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