6 citations · 6 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
The logic of rational graph neural networks
Sammy Khalife
The expressivity of Graph Neural Networks (GNNs) can be described via appropriate fragments of the first order logic. Any query of the two variable fragment of graded modal logic (…
On the power of graph neural networks and the role of the activation function
Sammy Khalife, Amitabh Basu
In this article we present new results about the expressivity of Graph Neural Networks (GNNs). We prove that for any GNN with piecewise polynomial activations, whose architecture s…
Neural networks with linear threshold activations: structure and algorithms
Sammy Khalife, Hongyu Cheng, Amitabh Basu
In this article we present new results on neural networks with linear threshold activation functions. We precisely characterize the class of functions that are representable by suc…