21 citations · 40 across the 17 of their papers we have counts for
20 papers
Markov Processes for Enhanced Deepfake Generation and Detection
Michael A. Kouritzin, Ian Zhang, Jyoti Bhadana +1
New and existing methods for generating, and especially detecting, deepfakes are investigated and compared on the simple problem of authenticating coin flip data. Importantly, an a…
Sampling and Filtering with Markov Chains
Michael A. Kouritzin
A continuous-time Markov chain rate change formula for simulation, model selection, filtering and theory is proven. It is used to develop Markov chain importance sampling, rejectio…
Universal Approximation and the Topological Neural Network
Michael A. Kouritzin, Daniel Richard
A topological neural network (TNN), which takes data from a Tychonoff topological space instead of the usual finite dimensional space, is introduced. As a consequence, a distributi…
A Bootstrap Algorithm for Fast Supervised Learning
Michael A Kouritzin, Stephen Styles, Beatrice-Helen Vritsiou
Training a neural network (NN) typically relies on some type of curve-following method, such as gradient descent (GD) (and stochastic gradient descent (SGD)), ADADELTA, ADAM or lim…
Markov Observation Models
Michael A. Kouritzin
Herein, the Hidden Markov Model is expanded to allow for Markov chain observations. In particular, the observations are assumed to be a Markov chain whose one step transition proba…
On mean-field super-Brownian motions
Yaozhong Hu, Michael A. Kouritzin, Panqiu Xia +1
The mean-field stochastic partial differential equation (SPDE) corresponding to a mean-field super-Brownian motion (sBm) is obtained and studied. In this mean-field sBm, the branch…