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Alessandro Artussi

dblp:241/4320 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Network and information security
1 paper
Security and privacy of machine learning · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › robustness
robust learning		 0.412019
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities · DAC 2019
Machine learning › Trustworthy machine learning
robustness		 0.412019
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities · DAC 2019
Security and privacy of machine learning
machine learning security		 0.112019
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities · DAC 2019
Embedded and real-time systems › critical systems
safety-critical systems		 0.112019
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities · DAC 2019

Methods — techniques the papers use, named apart from their topics

deep learning · 1.1
YearPublicationVenuePosition
2019 Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities
abstract
Machine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications.
Jeff Zhang 0001, Kang Liu 0017, Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman, Theocharis Theocharides, Alessandro Artussi, Muhammad Shafique 0001, Siddharth Garg
 DAC7