A new interpretable unsupervised anomaly detection method based on
residual explanation

A new interpretable unsupervised anomaly detection method based on residual explanation

14 March 2021

David F. N. Oliveira

A. M. Nascimento

J. R. de Almeida

Marcelo M. Neves

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Papers citing "A new interpretable unsupervised anomaly detection method based on residual explanation"

16 / 16 papers shown

Title
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 417 10,591 0 17 Feb 2020
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 48 103 0 04 Sep 2019
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks Sefi Akerman Edan Habler A. Shabtai 70 18 0 19 Jun 2019
Anomaly Detection in Images Manpreet Singh Minhas John S. Zelek 28 29 0 09 May 2019
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection Q. Nguyen Kar Wai Lim D. Divakaran K. H. Low M. Chan DRL 47 136 0 15 Mar 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 62 86 0 06 Mar 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 193 1,441 0 14 Jan 2019
Deep Learning for Anomaly Detection: A Survey Raghavendra Chalapathy Sanjay Chawla AI4TS 154 1,493 0 10 Jan 2019
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma Yuma Koizumi Shoichiro Saito Hisashi Uematsu Noboru Harada AAML 59 159 0 22 Oct 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 95 1,857 0 31 May 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,939 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 201 3,873 0 10 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 402 3,798 0 28 Feb 2017
Gradients of Counterfactuals Mukund Sundararajan Ankur Taly Qiqi Yan FAtt 59 104 0 08 Nov 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,701 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016

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