Ralph Abboud
Ralph Abboud
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
My research interests primarily lie at the intersection between deep learning and knowledge representation and reasoning. In particular, I am interested in developing hybrid neuro-symbolic systems, based on neural networks and explicit logical structures, to more efficiently and reliably learn patterns and make inferences from data. The main aim of my research is to produce systems that combine the learning capacity of deep neural networks with the reliability, safety, and interpretability of logical systems.
Biography
I earned a B.E. in Computer Engineering from the Lebanese American University (LAU), with a minor in Mathematics, in 2017, and my capstone project, supervised by Dr. Joe Tekli, was on automated sentiment-aware music composition using machine learning and genetic algorithms. I then completed an MSc in Computer Science at the University of Oxford in 2018, with my thesis, supervised by Prof. Daniel Kroening, tackling neural program synthesis. Parallel to my studies, I also earned a Baccalaureate degree in Piano performance from the Lebanese National Higher Conservatory of Music (LNHCM) in 2017.
Selected Publications
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Neural Networks for Approximate DNF Counting: An Abridged Report
Ralph Abboud‚ İsmail İlkan Ceylan and Thomas Lukasiewicz
In Lingfei Wu‚ Jian Tang‚ Yinglong Xia and Charu Aggarwal, editors, Proceedings of the 1st International Workshop on Deep Learning on Graphs: Methodologies and Applications‚ DLGMA 2020‚ New York‚ New York‚ USA‚ February 8‚ 2020. February, 2020.
Details about Neural Networks for Approximate DNF Counting: An Abridged Report | BibTeX data for Neural Networks for Approximate DNF Counting: An Abridged Report | Download (pdf) of Neural Networks for Approximate DNF Counting: An Abridged Report
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Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Ralph Abboud‚ İsmail İlkan Ceylan and Thomas Lukasiewicz
In Vincent Conitzer and Fei Sha, editors, Proceedings of the 34th AAAI Conference on Artificial Intelligence‚ AAAI 2020‚ New York‚ New York‚ USA‚ February 7–12‚ 2020. AAAI Press. February, 2020.
Details about Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting | BibTeX data for Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting | Link to Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
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On the Approximability of Weighted Model Integration on DNF Structures
Ralph Abboud‚ İsmail İlkan Ceylan and Radoslav Dimitrov
In Diego Calvanese and Esra Erdem, editors, Proceedings of 17th International Conference on Principles of Knowledge Representation and Reasoning‚ KR 2020‚ September 12−18‚ Rhodes‚ Greece. AAAI Press. 2020.
Details about On the Approximability of Weighted Model Integration on DNF Structures | BibTeX data for On the Approximability of Weighted Model Integration on DNF Structures | Download (pdf) of On the Approximability of Weighted Model Integration on DNF Structures