论文信息 - Multi-Class Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model - 字舞流文

Multi-Class Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model

Milad Memarzadeh | Bryan Matthews | Thomas Templin

[1] Michelle Kirby,et al. Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning , 2020, Aerospace.

[2] David Schleicher,et al. Discovery of Abnormal Flight Patterns in Flight Track Data , 2013 .

[3] Vijay Manikandan Janakiraman,et al. Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning , 2017, KDD.

[4] Milad Memarzadeh,et al. Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder , 2020, Aerospace.

[5] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .

[6] Loïc Le Folgoc,et al. Semi-Supervised Learning via Compact Latent Space Clustering , 2018, ICML.

[7] Alexander Zien,et al. Semi-Supervised Learning , 2006 .

[8] Stephen D. Bay,et al. Mining distance-based outliers in near linear time with randomization and a simple pruning rule , 2003, KDD '03.

[9] Ashok N. Srivastava,et al. Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study , 2010, KDD.

[10] Max Welling,et al. Semi-supervised Learning with Deep Generative Models , 2014, NIPS.