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 861–870 of 3073 papers

Title	Date	Tasks	Status	Score
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available	5
Active Learning with Contrastive Pre-training for Facial Expression Recognition	Jul 6, 2023	Active LearningFacial Expression Recognition	CodeCode Available	5
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing	Dec 27, 2021	Active LearningDiversity	CodeCode Available	5
Active Selection of Classification Features	Feb 26, 2021	Active LearningClassification	CodeCode Available	5
BAL: Balancing Diversity and Novelty for Active Learning	Dec 26, 2023	Active LearningDiversity	CodeCode Available	5
Active Learning for Deep Gaussian Process Surrogates	Dec 15, 2020	Active LearningGaussian Processes	CodeCode Available	5
Automated Performance Testing Based on Active Deep Learning	Apr 5, 2021	Active LearningDeep Learning	CodeCode Available	5
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments	Dec 15, 2024	Active LearningCausal Inference	CodeCode Available	5
Automated Seed Quality Testing System using GAN & Active Learning	Oct 2, 2021	Active LearningGenerative Adversarial Network	CodeCode Available	5
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images	Jul 7, 2020	Active Learning	CodeCode Available	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