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 221–230 of 3073 papers

Title	Date	Tasks	Status	Hype
On Minimizing Cost in Legal Document Review Workflows	Jun 18, 2021	Active LearningTAR	CodeCode Available	1
Gone Fishing: Neural Active Learning with Fisher Embeddings	Jun 17, 2021	Active Learning	CodeCode Available	1
Visual Transformer for Task-aware Active Learning	Jun 7, 2021	Active Learning	CodeCode Available	1
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design	Jun 7, 2021	Active LearningEvolutionary Algorithms	CodeCode Available	1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains	May 23, 2021	Active LearningBayesian Optimisation	CodeCode Available	1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning	May 21, 2021	Active LearningData Augmentation	CodeCode Available	1
Active WeaSuL: Improving Weak Supervision with Active Learning	Apr 30, 2021	Active Learning	CodeCode Available	1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning	Apr 24, 2021	Active LearningAnomaly Detection	CodeCode Available	1
A Mathematical Analysis of Learning Loss for Active Learning in Regression	Apr 19, 2021	Active LearningPose Estimation	CodeCode Available	1
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?	Apr 19, 2021	Active LearningPose Estimation	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