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 3011–3020 of 3073 papers

Title	Date	Tasks	Status
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment	Jun 1, 2013	Active LearningLanguage Modeling	—Unverified
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels	Jun 1, 2013	Active LearningRelation Extraction	—Unverified
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion	Jun 1, 2013	Active LearningGrapheme-to-Phoneme Conversion	—Unverified
Adaptive Active Learning for Image Classification	Jun 1, 2013	Active LearningClassification	—Unverified
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback	Jun 1, 2013	Active LearningAttribute	—Unverified
Machine learning of hierarchical clustering to segment 2D and 3D images	Mar 25, 2013	Active LearningBIG-bench Machine Learning	CodeCode Available
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots	Jan 21, 2013	Active Learning	CodeCode Available
Active Learning and the Irish Treebank	Dec 1, 2012	Active LearningMachine Translation	—Unverified
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature	Dec 1, 2012	Active Learning	—Unverified
Active Learning for Chinese Word Segmentation	Dec 1, 2012	Active LearningChinese Word Segmentation	—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