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cs.LG2024
Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks
Sammy Khalife, Josué Tonelli-Cueto
Uniform expressivity guarantees that a Graph Neural Network (GNN) can express a query without the parameters depending on the size of the input graphs. This property is desirable i…
cs.DS2024
Sequence graphs realizations and ambiguity in language models
Sammy Khalife, Yann Ponty, Laurent Bulteau
Several popular language models represent local contexts in an input text as bags of words. Such representations are naturally encoded by a sequence graph whose vertices are th…
cs.LG2024
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
Hongyu Cheng, Sammy Khalife, Barbara Fiedorowicz +1
Data-driven algorithm design is a paradigm that uses statistical and machine learning techniques to select from a class of algorithms for a computational problem an algorithm that…