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Found 7 papers, 1 papers with code
Minimum Width for Deep, Narrow MLP: A Diffeomorphism and the Whitney Embedding Theorem Approach
Recently, there has been significant attention on determining the minimum width for the universal approximation property of deep, narrow MLPs.
Minimal Width for Universal Property of Deep RNN
In this study, we prove the universality of deep narrow RNNs and show that the upper bound of the minimum width for universality can be independent of the length of the data.
Universal Property of Convolutional Neural Networks
A convolution with padding outputs the data of the same shape as the input data; therefore, it is necessary to prove whether a convolutional neural network composed of convolutions can approximate such a function.
Finding the global semantic representation in GAN through Frechet Mean
This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space.
Analyzing the Latent Space of GAN through Local Dimension Estimation
In this paper, we approach this problem through a geometric analysis of latent spaces as a manifold.
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.
Discond-VAE: Disentangling Continuous Factors from the Discrete
To represent these generative factors of data, we introduce two sets of continuous latent variables, private variable and public variable.
