Very Deep Convolutional Networks for Large-Scale Image Recognition

Karen Simonyan, Andrew Zisserman

arXiv (Cornell University)

Geometric LearningConvolutional Neural NetworkEngineeringMachine LearningConvolutional Network Depth +14

Visualizing Data using t-SNE

Laurens van der Maaten, Geoffrey E. Hinton

Journal of Machine Learning Research

EngineeringData VisualizationData ExplorationVisualization (Data Visualization)Linear Embedding +14

Principal component analysis

Svante Wold, Kim H. Esbensen, Paul Geladi

Chemometrics and Intelligent Laboratory Systems

EngineeringPattern RecognitionKnowledge DiscoveryMultilinear Subspace LearningIndependent Component Analysis +3

Isolation Forest

Fei Tony Liu, Kai Ming Ting, Zhi‐Hua Zhou

Anomaly DetectionMachine LearningData ScienceData MiningPattern Recognition +12

LOF

Markus Breunig, Hans‐Peter Kriegel, Raymond T. Ng, +1

ACM SIGMOD Record

Anomaly DetectionMachine LearningEngineeringPattern DiscoveryLocal Outliers +14

Support Vector Data Description

David M. J. Tax, Robert P. W. Duin

Machine Learning

Data ClassificationSupport Vector MachineMachine VisionImage AnalysisData Science +6

Principal component analysis

Rasmus Bro, Age K. Smilde

Analytical Methods

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Tero Karras, Timo Aila, Samuli Laine, +1

arXiv (Cornell University)

Image AnalysisMachine LearningData ScienceEngineeringGenerative Adversarial Network +12

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

Sarah Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, +1

Pattern Recognition

Support Vector MachineAnomaly DetectionMachine LearningData ScienceData Mining +9

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

Olivier Chapelle, Bernhard Schölkopf, Eds. A. Zien

IEEE Transactions on Neural Networks

Artificial IntelligenceIntelligent Information ProcessingEngineeringMachine LearningComputer Analysis +17