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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 261–270 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings	Oct 16, 2020	Active LearningClustering	CodeCode Available	1
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator	Oct 10, 2020	Active LearningClassification	CodeCode Available	1
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup	Oct 5, 2020	Active LearningData Augmentation	CodeCode Available	1
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation	Oct 5, 2020	Active LearningObject	CodeCode Available	1
Neural Bootstrapper	Oct 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
HUMAN: Hierarchical Universal Modular ANnotator	Oct 2, 2020	Active LearningManagement	CodeCode Available	1
Active Learning for Bayesian 3D Hand Pose Estimation	Oct 1, 2020	3D Hand Pose EstimationActive Learning	CodeCode Available	1
Multi-task Causal Learning with Gaussian Processes	Sep 27, 2020	Active LearningBayesian Optimization	CodeCode Available	1
Synbols: Probing Learning Algorithms with Synthetic Datasets	Sep 14, 2020	Active LearningFew-Shot Learning	CodeCode Available	1
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene	Aug 26, 2020	Active Learning	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