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Marco Maggipinto
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2020 – today
- 2023
- [j4]Federico Zocco
, Marco Maggipinto
Lazy FSCA for unsupervised variable selection. Eng. Appl. Artif. Intell. 124: 106624 (2023) - 2022
- [j3]Marco Maggipinto
IntroVAC: Introspective Variational Classifiers for learning interpretable latent subspaces. Eng. Appl. Artif. Intell. 109: 104658 (2022) - [j2]Marco Maggipinto, Alessandro Beghi
A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study. IEEE Trans Autom. Sci. Eng. 19(3): 1477-1490 (2022) - 2021
- [c8]Matteo Terzi, Alessandro Achille, Marco Maggipinto, Gian Antonio Susto:
Adversarial Training Reduces Information and Improves Transferability. AAAI 2021: 2674-2682 - [i5]Federico Zocco, Marco Maggipinto, Gian Antonio Susto, Seán F. McLoone:
Greedy Search Algorithms for Unsupervised Variable Selection: A Comparative Study. CoRR abs/2103.02687 (2021) - 2020
- [j1]Gian Antonio Susto
Induced Start Dynamic Sampling for Wafer Metrology Optimization. IEEE Trans Autom. Sci. Eng. 17(1): 418-432 (2020) - [c7]Marco Maggipinto, Gian Antonio Susto
Proximal Deterministic Policy Gradient. IROS 2020: 5438-5444 - [c6]Mattia Carletti
Interpretable Anomaly Detection for Knowledge Discovery in Semiconductor Manufacturing. WSC 2020: 1875-1885 - [i4]Matteo Terzi, Alessandro Achille, Marco Maggipinto, Gian Antonio Susto:
Adversarial Training Reduces Information and Improves Transferability. CoRR abs/2007.11259 (2020) - [i3]Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari:
Proximal Deterministic Policy Gradient. CoRR abs/2008.00759 (2020) - [i2]Marco Maggipinto, Matteo Terzi, Gian Antonio Susto:
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces. CoRR abs/2008.00760 (2020) - [i1]Marco Maggipinto, Matteo Terzi, Gian Antonio Susto:
β-Variational Classifiers Under Attack. CoRR abs/2008.09010 (2020)
2010 – 2019
- 2019
- [c5]Marco Maggipinto, Gian Antonio Susto
What are the Most Informative Data for Virtual Metrology? A use case on Multi-Stage Processes Fault Prediction. CASE 2019: 1796-1801 - [c4]Alberto Purpura, Marco Maggipinto, Gianmaria Silvello
Probabilistic Word Embeddings in Neural IR: A Promising Model That Does Not Work as Expected (For Now). ICTIR 2019: 3-10 - [c3]Marco Maggipinto, Alessandro Beghi, Gian Antonio Susto
A Deep Learning-based Approach to Anomaly Detection with 2-Dimensional Data in Manufacturing. INDIN 2019: 187-192 - 2018
- [c2]Gian Antonio Susto
Machine Learning-based Laundry Weight Estimation for Vertical Axis Washing Machines. ECC 2018: 3179-3184 - 2017
- [c1]Matteo Terzi
Deep learning for virtual metrology: Modeling with optical emission spectroscopy data. RTSI 2017: 1-6
Coauthor Index
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