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Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete
  Events Based On Autoencoder

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder

27 December 2020
Lun-Pin Yuan
Peng Liu
Sencun Zhu
    AI4TS
ArXivPDFHTML

Papers citing "Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder"

15 / 15 papers shown
Title
Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction
Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction
Francesco Vitale
Marco Pegoraro
W. V. Aalst
Nicola Mazzocca
103
1
0
17 Feb 2025
GEE: A Gradient-based Explainable Variational Autoencoder for Network
  Anomaly Detection
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Q. Nguyen
Kar Wai Lim
D. Divakaran
K. H. Low
M. Chan
DRL
24
135
0
15 Mar 2019
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly
  prior knowledge for anomaly detection
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection
Xuhong Wang
Ying Du
Shijie Lin
Ping Cui
Yuntian Shen
Yupu Yang
DRL
ViT
UQCV
42
100
0
03 Mar 2019
Deep Learning for Anomaly Detection: A Survey
Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy
Sanjay Chawla
AI4TS
142
1,491
0
10 Jan 2019
Recurrent Neural Network Attention Mechanisms for Interpretable System
  Log Anomaly Detection
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Andy Brown
Aaron Tuor
Brian Hutchinson
Nicole Nichols
35
173
0
13 Mar 2018
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion
  Detection
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
Yisroel Mirsky
Tomer Doitshman
Yuval Elovici
A. Shabtai
74
811
0
25 Feb 2018
A Zero-Positive Learning Approach for Diagnosing Software Performance
  Regressions
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Mejbah Alam
Justin Emile Gottschlich
Nesime Tatbul
Javier S. Turek
Tim Mattson
A. Muzahid
40
29
0
21 Sep 2017
Mimicking Word Embeddings using Subword RNNs
Mimicking Word Embeddings using Subword RNNs
Yuval Pinter
Robert Guthrie
Jacob Eisenstein
58
159
0
21 Jul 2017
Robust, Deep and Inductive Anomaly Detection
Robust, Deep and Inductive Anomaly Detection
Raghavendra Chalapathy
A. Menon
Sanjay Chawla
61
161
0
22 Apr 2017
LSTM: A Search Space Odyssey
LSTM: A Search Space Odyssey
Klaus Greff
R. Srivastava
Jan Koutník
Bas R. Steunebrink
Jürgen Schmidhuber
AI4TS
VLM
112
5,290
0
13 Mar 2015
Unsupervised Learning of Video Representations using LSTMs
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava
Elman Mansimov
Ruslan Salakhutdinov
SSL
130
2,589
0
16 Feb 2015
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log
  Data
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Alina Oprea
Zhou Li
T. Yen
Sang Chin
Sumayah A. Alrwais
51
171
0
18 Nov 2014
Learning Phrase Representations using RNN Encoder-Decoder for
  Statistical Machine Translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho
B. V. Merrienboer
Çağlar Gülçehre
Dzmitry Bahdanau
Fethi Bougares
Holger Schwenk
Yoshua Bengio
AIMat
900
23,310
0
03 Jun 2014
How to Construct Deep Recurrent Neural Networks
How to Construct Deep Recurrent Neural Networks
Razvan Pascanu
Çağlar Gülçehre
Kyunghyun Cho
Yoshua Bengio
111
1,009
0
20 Dec 2013
Speech Recognition with Deep Recurrent Neural Networks
Speech Recognition with Deep Recurrent Neural Networks
Alex Graves
Abdel-rahman Mohamed
Geoffrey E. Hinton
202
8,507
0
22 Mar 2013
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