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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 1261–1270 of 3073 papers

Title	Date	Tasks	Status	Hype
Federated Learning with Uncertainty via Distilled Predictive Distributions	Jun 15, 2022	Active LearningDecision Making	—Unverified	0
Physics-informed EDFA Gain Model Based on Active Learning	Jun 13, 2022	Active Learning	—Unverified	0
On the reusability of samples in active learning	Jun 13, 2022	Active Learning	CodeCode Available	5
Efficient Human-in-the-loop System for Guiding DNNs Attention	Jun 13, 2022	Active Learningimage-classification	CodeCode Available	0
Weighted Ensembles for Active Learning with Adaptivity	Jun 10, 2022	Active Learning	—Unverified	0
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning	Jun 10, 2022	Active LearningDiversity	—Unverified	0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning	Jun 9, 2022	Active LearningDrug Design	—Unverified	0
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning	Jun 7, 2022	Active Learning	—Unverified	0
Active Bayesian Causal Inference	Jun 4, 2022	Active LearningCausal Discovery	CodeCode Available	1
Indirect Active Learning	Jun 3, 2022	Active Learning	—Unverified	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