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pro vyhledávání: '"Villani, Mattia Jacopo"'
The transformer neural network has significantly out-shined all other neural network architectures as the engine behind large language models. We provide a theoretical analysis of the expressivity of the transformer architecture through the lens of t
Externí odkaz:
http://arxiv.org/abs/2403.18415
Autor:
Villani, Mattia Jacopo, Schoots, Nandi
We constructively prove that every deep ReLU network can be rewritten as a functionally identical three-layer network with weights valued in the extended reals. Based on this proof, we provide an algorithm that, given a deep ReLU network, finds the e
Externí odkaz:
http://arxiv.org/abs/2306.11827
Deep ReLU Networks can be decomposed into a collection of linear models, each defined in a region of a partition of the input space. This paper provides three results extending this theory. First, we extend this linear decompositions to Graph Neural
Externí odkaz:
http://arxiv.org/abs/2305.09424