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Found 7 papers, 0 papers with code
Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks
Uniform expressivity guarantees that a Graph Neural Network (GNN) can express a query without the parameters depending on the size of the input graphs.
Sequence graphs realizations and ambiguity in language models
For a window of size at least 3, we prove hardness of all variants, even when w is considered as a constant, with the notable exception of the undirected/unweighted case for which we propose an XP algorithms for both (realizability and enumeration) problems, tight due to a corresponding W[1]-hardness result.
Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
We build upon recent work in this line of research by considering the setup where, instead of selecting a single algorithm that has the best performance, we allow the possibility of selecting an algorithm based on the instance to be solved, using neural networks.
The logic of rational graph neural networks
In this article, we prove that some GC2 queries of depth $3$ cannot be expressed by GNNs with any rational activation function.
On the power of graph neural networks and the role of the activation function
In contrast, it was already known that unbounded GNNs (those whose size is allowed to change with the graph sizes) with piecewise polynomial activations can distinguish these vertices in only two iterations.
Neural networks with linear threshold activations: structure and algorithms
We precisely characterize the class of functions that are representable by such neural networks and show that 2 hidden layers are necessary and sufficient to represent any function representable in the class.
Scalable graph-based method for individual named entity identification
In this paper, we consider the named entity linking (NEL) problem.
