In generative modeling, numerous successful approaches leverage a low-dimensional
latent space, eg, Stable Diffusion models the latent space induced by an encoder and …

Neural implicit surfaces are a promising tool for geometry processing that represent a solid
object as the zero level set of a neural network. Usually trained to approximate a signed …

We consider a binary classifier defined as the sign of a tropical rational function, that is, as
the difference of two convex piecewise linear functions. The parameter space of ReLU …

Current theoretical and empirical research in neural networks suggests that complex
datasets require large network architectures for thorough classification, yet the precise …

We develop a geometric approximation theory for deep feed-forward neural networks with
ReLU activations. Given a $ d $-dimensional hypersurface in $\mathbb {R}^{d+ 1} …