Now showing items 1-5 of 5
Emergent behavior in neuroevolved agents
(University of Wisconsin--Whitewater, 2018-11)
Neural networks have been widely used for their ability to create generalized rulesets for a given set of training data. In applications where no such training data exists such as new video games, they are often overlooked ...
A residual recurrent convolutional neural network for image superresolution with whole slide images
(University of Wisconsin--Whitewater, 2019-04)
Presented is a deep learning based computational approach to solve the problem of enhancing the resolution of images gained from commonly available low magnification scanners, also known as the image super-resolution (SR) ...
Modeling user behavior to construct counter strategies
(University of Wisconsin--Whitewater, 2019-08)
We are working on the development of an adaptive learning framework addressing covariate shift, experienced in Behavioral Cloning (BC). BC user-modeling is a technique in which user-data, taken from observing a user’s ...
Solving non-decomposable objectives using linear programming layers in general machine learning models : SVMs and deep neural networks
(University of Wisconsin - Whitewater, 2021-05)
Many domain specific machine learning tasks require more fine tuning with respect to nondecomposable metrics to be effective. In many applications such as medical diagnosis and fraud detection, traditional loss measures ...
Audio generation from a single sample using deep convolutional generative adversarial networks
(University of Wisconsin - Whitewater, 2021-12)
Training neural networks require sizeable datasets for meaningful output. It is difficult to acquire large datasets for many types of data. This is especially challenging for individuals and small organizations. We have ...