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 2021–2030 of 3073 papers

Title	Date	Tasks	Status
Pre-trained Language Model Based Active Learning for Sentence Matching	Oct 12, 2020	Active LearningLanguage Modeling	—Unverified
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks	Oct 10, 2020	Active LearningOpen-Domain Question Answering	CodeCode Available
Zero-shot Active Learning with Topological Clustering for Multiclass Classification	Oct 10, 2020	Active LearningClustering	—Unverified
Model Exploration with Cost-Aware Learning	Oct 9, 2020	Active LearningBIG-bench Machine Learning	—Unverified
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool	Oct 8, 2020	Active Learningtext annotation	—Unverified
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective	Oct 7, 2020	Active LearningMulti-Armed Bandits	—Unverified
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps	Oct 5, 2020	Active LearningImage Segmentation	—Unverified
Data-efficient Online Classification with Siamese Networks and Active Learning	Oct 4, 2020	Active LearningClassification	—Unverified
Gaussian Process Molecule Property Prediction with FlowMO	Oct 2, 2020	Active LearningGaussian Processes	—Unverified
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification	Sep 30, 2020	Active LearningData Augmentation	—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