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 241–250 of 3073 papers

Title	Date	Tasks	Status	Hype
Discrepancy-Based Active Learning for Domain Adaptation	Mar 5, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Active learning based generative design for the discovery of wide bandgap materials	Feb 28, 2021	Active LearningBand Gap	CodeCode Available	1
Deep Deterministic Uncertainty: A Simple Baseline	Feb 23, 2021	Active LearningUncertainty Quantification	CodeCode Available	1
SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology	Feb 23, 2021	Active LearningGeneral Classification	CodeCode Available	1
DEUP: Direct Epistemic Uncertainty Prediction	Feb 16, 2021	Active Learningimage-classification	CodeCode Available	1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks	Jan 27, 2021	Active LearningUncertainty Quantification	CodeCode Available	1
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms	Dec 29, 2020	Active Learning	CodeCode Available	1
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning	Dec 19, 2020	Active Learning	CodeCode Available	1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking	Dec 14, 2020	Active LearningContrastive Learning	CodeCode Available	1
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	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