11 citations · 11 across the 3 of their papers we have counts for
7 papers · 1 filter
Deterministic Bounds and Random Estimates of Metric Tensors on Neuromanifolds
Ke Sun
The high-dimensional parameter space of deep neural networks -- the neuromanifold -- is endowed with a unique metric tensor defined by the Fisher information. Reliable and scalable…
On the Variance of the Fisher Information for Deep Learning
Alexander Soen, Ke Sun
In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and…
Fisher-Bures Adversary Graph Convolutional Networks
Ke Sun, Piotr Koniusz, Zhen Wang
In a graph convolutional network, we assume that the graph is generated wrt some observation noise. During learning, we make small random perturbations of the graph and tr…
On The Chain Rule Optimal Transport Distance
Frank Nielsen, Ke Sun
We define a novel class of distances between statistical multivariate distributions by modeling an optimal transport problem on their marginals with respect to a ground distance de…
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures
Frank Nielsen, Ke Sun
The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in . This distance plays…
Coarse Grained Exponential Variational Autoencoders
Ke Sun, Xiangliang Zhang
Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process. This puts a limit on variational learning because this simplified assumpt…