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Found 3 papers, 1 papers with code
Algebraic and Geometric Models for Space Networking
In this paper we introduce some new algebraic and geometric perspectives on networked space communications.
On the Representation Power of Set Pooling Networks
Since sets are inherently unaffected by permutations, the input space for these problems naturally forms a non-Euclidean space whose topology depends on the task and which is oftentimes not even a manifold.
Characterizing the Shape of Activation Space in Deep Neural Networks
The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure.
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