… can be inferred from the context we simplify the terminology of a directed map network as a
map network. The following definition considers induced maps along paths of a map network. …

… network really extracting generalized path‐invariant features? Or, is it merely memorizing
all the training paths exactly, in a way that appears to support conclusions that are unwarranted…

… We use regional and global seismic event catalogs to explore feature generation with deep
neural networks. We use methods that encourage neural networks to learn source related …

… potential as drone base stations creating a dynamic radio access network for plugging
coverage holes in terrestrial mobile and cellular networks [5]. Most use cases demand a payload-…

… network effects via a properly tuned recurrent network (35). … the connectivity of a path-invariant
network. Computationally, … whether a model is path invariant. Experimentally, verifying …

… the context of formal models of network protocols, in our case, … Our path invariant generation
procedures are fully generic … counterexample with the generated path invariant. We formally …

… Besides, adding batch normalization layers to the network makes its forward path invariant
to the linear scaling of its weight parameters [17]. The above introduced symmetry or …

… should be path invariant: gT … network to learn path invariant representations and continuous
attractor dynamics, and the dynamical translation operation of the recurrent neural networks …

… The average shortest path invariant, C asp , is a measure of the efficiency of a vertex in
transferring information in the whole network. It can be defined as the reciprocal of nodal …

… It is poised to serve as a foundational infrastructure for enabling future networks, providing
… In the sequel, without loss of generality, we focus on one path-invariant block and drop the …