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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 151–160 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning	Jan 23, 2019	Active LearningAnomaly Detection	CodeCode Available	1	5
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning	Apr 16, 2021	Active LearningNatural Language Understanding	CodeCode Available	1	5
Active Bayesian Causal Inference	Jun 4, 2022	Active LearningCausal Discovery	CodeCode Available	1	5
Bayesian Optimization with Conformal Prediction Sets	Oct 22, 2022	Active LearningBayesian Optimization	CodeCode Available	1	5
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	CodeCode Available	1	5
Biological Sequence Design with GFlowNets	Mar 2, 2022	Active LearningDiversity	CodeCode Available	1	5
Active Imitation Learning with Noisy Guidance	May 26, 2020	Active LearningImitation Learning	CodeCode Available	1	5
Active Learning at the ImageNet Scale	Nov 25, 2021	Active Learning	CodeCode Available	1	5
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets	Feb 6, 2022	Active Learning	CodeCode Available	1	5
Active Learning for BERT: An Empirical Study	Nov 1, 2020	Active LearningBinary text classification	CodeCode Available	1	5

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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