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Edoardo Mosca
Researcher at Technische Universität München
Publications - 5
Citations - 37
Edoardo Mosca is an academic researcher from Technische Universität München. The author has contributed to research in topics: Deep learning & Software. The author has an hindex of 2, co-authored 5 publications receiving 5 citations.
Papers
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Proceedings ArticleDOI
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
TL;DR: This work reveals that user features play a role in the model’s decision and how they affect the feature space learned by the model, and shows how such techniques can be combined to better understand the model and to detect unintended bias.
Journal ArticleDOI
Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation
Lorenzo Servadei,Edoardo Mosca,Elena Zennaro,Keerthikumara Devarajegowda,Michael Werner,Wolfgang Ecker,Robert Wille +6 more
TL;DR: This article borrows and re-adapt solutions based on Machine Learning (ML) which have been found suitable for problems from the domain of Computer Vision (CV) and proposes an approach which outperforms existing methods for cost estimation.
Book ChapterDOI
Explainable Abusive Language Classification Leveraging User and Network Data
TL;DR: In this article, an explainable AI framework SHAP (SHapley Additive explanations) is proposed to alleviate the general issue of missing transparency associated with deep learning models, allowing the model to assess the model's vulnerability toward bias and systematic discrimination reliably.
Proceedings ArticleDOI
Cost Estimation for Configurable Model-Driven SoC Designs Using Machine Learning
Lorenzo Servadei,Edoardo Mosca,Keerthikumara Devarajegowda,Michael Werner,Wolfgang Ecker,Robert Wille +5 more
TL;DR: A key element of the proposed method is a data representation which describes SoC configurations in a way that is suited for advanced ML algorithms, which helps to structure a cost estimation method that supports multiple configurations of an SoC.
Proceedings ArticleDOI
Combining Evolutionary Algorithms and Deep Learning for Hardware/Software Interface Optimization
TL;DR: This work introduces a novel optimization method for minimizing the cost of Hardware/Software Interfaces using Convolutional Neural Networks coupled with Evolutionary Algorithms.