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 1331–1340 of 3073 papers

Title	Date	Tasks	Status
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks	Oct 12, 2022	Active LearningGraph Mining	—Unverified
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes	Oct 11, 2022	Active LearningKnowledge Distillation	—Unverified
Deep Active Ensemble Sampling For Image Classification	Oct 11, 2022	Active LearningClassification	—Unverified
Few-Shot Continual Active Learning by a Robot	Oct 9, 2022	Active LearningContinual Learning	—Unverified
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design	Oct 8, 2022	Active LearningBayesian Optimization	—Unverified
Is margin all you need? An extensive empirical study of active learning on tabular data	Oct 7, 2022	Active LearningAll	—Unverified
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models	Oct 6, 2022	Active Learning	CodeCode Available
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures	Oct 4, 2022	Active Learning	—Unverified
Active Learning for Regression with Aggregated Outputs	Oct 4, 2022	Active LearningGaussian Processes	—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