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 1691–1700 of 3073 papers

Title	Date	Tasks	Status
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers	Sep 22, 2021	Active LearningAdversarial Attack	—Unverified
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments	Sep 20, 2021	Active Learning	CodeCode Available
Towards Overcoming Practical Obstacles to Deploying Deep Active Learning	Sep 17, 2021	Active Learningtext-classification	—Unverified
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks	Sep 16, 2021	Active Learning	—Unverified
FOMO: Topics versus documents in legal eDiscovery	Sep 16, 2021	Active Learning	—Unverified
Mitigating Sampling Bias and Improving Robustness in Active Learning	Sep 13, 2021	Active Learning	—Unverified
Robust Contrastive Active Learning with Feature-guided Query Strategies	Sep 13, 2021	Active Learningimage-classification	—Unverified
Adaptive network reliability analysis: Methodology and applications to power grid	Sep 11, 2021	Active Learning	—Unverified
Active learning for reducing labeling effort in text classification tasks	Sep 10, 2021	Active Learningtext-classification	CodeCode Available
Open-World Active Learning with Stacking Ensemble for Self-Driving Cars	Sep 10, 2021	Active LearningSelf-Driving Cars	—Unverified

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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