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 2061–2070 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples	Jun 28, 2020	Active LearningDeep Reinforcement Learning	—Unverified	0
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output	Jun 26, 2020	Active LearningDiagnostic	CodeCode Available	1
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment	Jun 25, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization	Jun 24, 2020	Active LearningBayesian Optimization	CodeCode Available	0
Practical applications of metric space magnitude and weighting vectors	Jun 24, 2020	Active LearningBoundary Detection	—Unverified	0
MCAL: Minimum Cost Human-Machine Active Labeling	Jun 24, 2020	Active Learning	CodeCode Available	0
Graph Policy Network for Transferable Active Learning on Graphs	Jun 24, 2020	Active Learning	CodeCode Available	1
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure	Jun 22, 2020	Active Learning	—Unverified	0
Open Source Software for Efficient and Transparent Reviews	Jun 22, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification	Jun 22, 2020	Active LearningExperimental Design	CodeCode Available	1

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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