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Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes
We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width.
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change
There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time.
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