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 281–290 of 3073 papers

Title	Date	Tasks	Status	Hype
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification	Jun 22, 2020	Active LearningExperimental Design	CodeCode Available	1
Sequential Graph Convolutional Network for Active Learning	Jun 18, 2020	Active LearningHand Pose Estimation	CodeCode Available	1
Bayesian active learning for production, a systematic study and a reusable library	Jun 17, 2020	Active Learning	CodeCode Available	1
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	CodeCode Available	1
Learning compositional models of robot skills for task and motion planning	Jun 8, 2020	Active LearningMotion Planning	CodeCode Available	1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge	Jun 1, 2020	Active LearningDomain Adaptation	CodeCode Available	1
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity	May 29, 2020	Active LearningAnomaly Detection	CodeCode Available	1
Active Imitation Learning with Noisy Guidance	May 26, 2020	Active LearningImitation Learning	CodeCode Available	1
Active Learning for Coreference Resolution using Discrete Annotation	Apr 28, 2020	Active LearningClustering	CodeCode Available	1
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model	Mar 31, 2020	Active LearningKnowledge Distillation	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