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 ...

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 ...

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 ...

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) ...

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 ...