Fetching the paper…
Reading the bibliography…
Originating from condensed matter physics, tensor networks are compact representations of high-dimensional tensors.
Density matrix formulation for quantum renormalization groups
Steven R. White · 1992
Earlier work this paper cites.
Thermodynamic limit of density matrix renormalization
Stellan Östlund and Stefan Rommer · 1995
Earlier work this paper cites.
On optimizing a class of multi-dimensional loops with reduction for parallel execution
Lam Chi-Chung, P. Sadayappan, and Rephael Wenger · 1997
Earlier work this paper cites.
Outlier detection using replicator neural networks
Simon Hawkins, Hongxing He, Graham J. Williams, and Rohan A. Baxter · 2002
Earlier work this paper cites.
One-class svms for document classification
Larry M. Manevitz and Malik Yousef · 2002
Earlier work this paper cites.
Support vector data description
David M. J. Tax and Robert P. W. Duin · 2004
Earlier work this paper cites.
Renormalization algorithms for quantum-many body systems in two and higher dimensions, 2004
F. Verstraete and J. I. Cirac · 2004
Earlier work this paper cites.
Isolation forest
F. T. Liu, K. M. Ting, and Z. Zhou · 2008
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Stacked convolutional auto-encoders for hierarchical feature extraction
Jonathan Masci, Ueli Meier, Dan Cireşan, and Jürgen Schmidhuber · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
The density-matrix renormalization group in the age of matrix product states
Ulrich Schollwöck · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
A practical introduction to tensor networks: Matrix product states and projected entangled pair states
Román Orús · 2014
Earlier work this paper cites.
Anomaly detection using autoencoders with nonlinear dimensionality reduction
Mayu Sakurada and Takehisa Yairi · 2014
Cited alongside, same era.
Winner-take-all autoencoders
Alireza Makhzani and Brendan Frey · 2015
Cited alongside, same era.
Learning deep representations of appearance and motion for anomalous event detection, 2015
Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and Nicu Sebe · 2015
Cited alongside, same era.
Detecting anomalous data using auto-encoders
Jerone Andrews, Edward Morton, and Lewis Griffin · 2016
Cited alongside, same era.
Adversarial feature learning, 2016
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Cited alongside, same era.
High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 2016
Anomaly detection using one-class neural networks, 2018
Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla · 2018
Later among the works it cites.
Image anomaly detection with generative adversarial networks
Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, and Marius Kloft · 2018
Later among the works it cites.
Unsupervised representation learning by predicting image rotations, 2018
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Later among the works it cites.
Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
Later among the works it cites.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Later among the works it cites.
Efficient gan-based anomaly detection, 2018
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
ODDS library, 2016
Shebuti Rayana · 2016
Cited alongside, same era.
Supervised learning with quantum-inspired tensor networks, 2016
E. Miles Stoudenmire and David J. Schwab · 2016
Cited alongside, same era.
Tensor networks in a nutshell, 2017
Jacob Biamonte and Ville Bergholm · 2017
Cited alongside, same era.
Outlier detection with autoencoder ensembles
Jinghui Chen, Saket Sathe, Charu C. Aggarwal, and Deepak S. Turaga · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Safe visual navigation via deep learning and novelty detection
Charles Richter and Nicholas Roy · 2017
Cited alongside, same era.
Later among the works it cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
Later among the works it cites.
Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon · 2019
Later among the works it cites.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Later among the works it cites.
Tensornetwork: A library for physics and machine learning, 2019
Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, and Stefan Leichenauer · 2019
Later among the works it cites.
Unsupervised identification of disease marker candidates in retinal oct imaging data
Philipp Seebock, Sebastian M. Waldstein, Sophie Klimscha, Hrvoje Bogunovic, Thomas Schlegl, Bianca S. Gerendas, Rene Donner, Ursula Schmidt-Erfurth, and Georg Langs · 2019
Later among the works it cites.
Classification-based anomaly detection for general data
Liron Bergman and Yedid Hoshen · 2020
Closest in time.
A multi-scale tensor network architecture for classification and regression, 2020
Justin Reyes and Miles Stoudenmire · 2020
Closest in time.
Tensor networks for medical image classification, 2020
Raghavendra Selvan and Erik B Dam · 2020
Closest in time.