Home Publications

Electronic pre-prints of the following publications are available only for personal use and must abide to copyrights of the publisher.   

IEEE-copyright notice: Copyright 199x IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Elsevier copyrights: pamphlet

IFAC copyrights notice

 

 

Reporitory of past Ph.D. dissertations

Publications Lists     [ all BibTeX ]  

Publications of keyword: Deep Learning
20YY
M. Maggipinto, M. Terzi, G.A. Susto. IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces. 20YY    [ abstract ] [url] [BibTeX]  
2021
M. Terzi, A. Achille, M. Maggipinto, G.A. Susto. Adversarial Training Reduces Information and Improves Transferability. 35th AAAI Conference on Artificial Intelligence, (arXiv:2007.11259), 2021    [ abstract ] [url] [BibTeX]  
R. Fantinel, A. Cenedese, G. Fadel. Hybrid Learning Driven by Dynamic Descriptors for Video Classification of Reflective Surfaces. IEEE Transactions on Industrial Informatics [early access], 2021    [ abstract ] [url] [BibTeX]  
2020
D. Tosato, D. Dalle Pezze, C. Masiero, G.A. Susto, A. Beghi. Alarm Logs in Packaging Industry (ALPI). IEEEDataPort, 2020    [ abstract ] [url] [BibTeX]  
M. Maggipinto, M. Terzi, G.A. Susto. Beta-Variational Classifiers Under Attack. IFAC World Congress, 2020   [BibTeX]  
M. Terzi, G.A. Susto, P. Chaudhari. Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications. Engineering Applications of Artificial Intelligence, vol. 91, 2020    [ abstract ] [url] [BibTeX]  
N. Gentner, M. Carletti, G.A. Susto, A. Kyek, Y. Yang. Enhancing Scalability of Virtual Metrology: a Deep Learning-based Approach for Domain Adaptation. Winter Simulation Conference, 2020    [ abstract ] [BibTeX]  
R. Fantinel, A. Cenedese. Multistep hybrid learning: CNN driven by spatial–temporal features for faults detection on metallic surfaces. Journal of Electronic Imaging, vol. 4, pp. 29, 2020    [ abstract ] [url] [BibTeX]  
T. Barbariol, D. Masiero, E. Feltresi, G.A. Susto. Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meter. North Sea Flow Measurement Workshop, 2020   [BibTeX]  
2019
M. Carletti, C. Masiero, A. Beghi, G.A. Susto. A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study. Procedia Manufacturing, vol. 38, pp. 233-240, 2019    [ abstract ] [url] [BibTeX]  
L. Brunelli, C. Masiero, D. Tosato, A. Beghi, G.A. Susto. Deep Learning-based Production Forecasting in Manufacturing: a Packaging Equipment Case Study. Procedia Manufacturing, vol. 38, pp. 248-255, 2019    [ abstract ] [BibTeX]  
2017
M. Terzi, C. Masiero, A. Beghi, M. Maggipinto, G.A. Susto. Deep Learning for Virtual Metrology: Modeling with Optical Emission Spectroscopy Data. IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017    [ abstract ] [url] [BibTeX]