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
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning	Dec 7, 2022	Active LearningEfficient Exploration	—Unverified
Dissimilar Nodes Improve Graph Active Learning	Dec 5, 2022	Active LearningGraph Embedding	CodeCode Available
Active learning using adaptable task-based prioritisation	Dec 3, 2022	Active LearningMeta Reinforcement Learning	—Unverified
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction	Dec 2, 2022	Active LearningGraph Learning	—Unverified
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data	Dec 2, 2022	Active LearningGenerative Adversarial Network	—Unverified
Deep Active Learning for Multi-Label Classification of Remote Sensing Images	Dec 2, 2022	Active LearningClustering	—Unverified
Margin-based sampling in high dimensions: When being active is less efficient than staying passive	Dec 1, 2022	Active Learning	—Unverified
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification	Nov 30, 2022	Active Learningimage-classification	—Unverified
ALARM: Active LeArning of Rowhammer Mitigations	Nov 30, 2022	Active Learning	—Unverified
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning	Nov 28, 2022	Active Learningreinforcement-learning	—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