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 2081–2090 of 3073 papers

Title	Date	Tasks	Status	Hype
Fourier Sparse Leverage Scores and Approximate Kernel Learning	Jun 12, 2020	Active LearningOpen-Ended Question Answering	—Unverified	0
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning	Jun 11, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	CodeCode Available	1
Dialog Policy Learning for Joint Clarification and Active Learning Queries	Jun 9, 2020	Active LearningImage Retrieval	—Unverified	0
Learning compositional models of robot skills for task and motion planning	Jun 8, 2020	Active LearningMotion Planning	CodeCode Available	1
Sophisticated Inference	Jun 7, 2020	Active Learningcounterfactual	—Unverified	0
How useful is Active Learning for Image-based Plant Phenotyping?	Jun 7, 2020	Active LearningClassification	CodeCode Available	0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance	Jun 6, 2020	Active LearningAttribute	—Unverified	0
Aspect-based Sentiment Analysis of Scientific Reviews	Jun 5, 2020	8kActive Learning	CodeCode Available	0
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	0

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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