Deep learning for virtual metrology: Modeling with optical emission spectroscopy data. - Matteo Terzi; Chiara Masiero; Alessandro Beghi; Marco Maggipinto; Gian Antonio Susto (RTSI, 2017)

Machine Learning-based Laundry Weight Estimation for Vertical Axis Washing Machines. - Gian Antonio Susto; Marco Maggipinto; Giulia Zannon; Fabio Altinier; Elena Pesavento; Alessandro Beghi (ECC, 2018)

What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction. - Marco Maggipinto; Gian Antonio Susto; Federico Zocco; Seán F. McLoone (CASE, 2019)

Probabilistic Word Embeddings in Neural IR: A Promising Model That Does Not Work as Expected (For Now). - Alberto Purpura; Marco Maggipinto; Gianmaria Silvello; Gian Antonio Susto (ICTIR, 2019)

A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing. - Marco Maggipinto; Alessandro Beghi; Gian Antonio Susto (INDIN, 2019)

Proximal Deterministic Policy Gradient. - Marco Maggipinto; Gian Antonio Susto; Pratik Chaudhari (IROS, 2020)

Interpretable Anomaly Detection for Knowledge Discovery in Semiconductor Manufacturing. - Mattia Carletti; Marco Maggipinto; Alessandro Beghi; Gian Antonio Susto; Natalie Gentner; Yao Yang; Andreas Kyek (WSC, 2020)

β-Variational Classifiers Under Attack. - Marco Maggipinto; Matteo Terzi; Gian Antonio Susto (CoRR, 2020)

Induced Start Dynamic Sampling for Wafer Metrology Optimization. - Gian Antonio Susto; Marco Maggipinto; Federico Zocco; Seán F. McLoone (IEEE Trans Autom. Sci. Eng., 2020)

Adversarial Training Reduces Information and Improves Transferability. - Matteo Terzi; Alessandro Achille; Marco Maggipinto; Gian Antonio Susto (AAAI, 2021)

Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study. - Federico Zocco; Marco Maggipinto; Gian Antonio Susto; Seán F. McLoone (CoRR, 2021)

IntroVAC: Introspective Variational Classifiers for learning interpretable latent subspaces. - Marco Maggipinto; Matteo Terzi; Gian Antonio Susto (Eng. Appl. Artif. Intell., 2022)

A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study. - Marco Maggipinto; Alessandro Beghi; Gian Antonio Susto (IEEE Trans Autom. Sci. Eng., 2022)

Lazy FSCA for unsupervised variable selection. - Federico Zocco; Marco Maggipinto; Gian Antonio Susto; Seán F. McLoone (Eng. Appl. Artif. Intell., 2023)