Eigen value based loss function for training attractors in iterated autoencoders.

The way that the human brain handles the input variations has been one of the most interesting areas of research for neuroscientists. There are some evidences that the human brain acts like an attractor when trying to memorize or retrieve some information. Based on this fact, in this research, a new method is presented for creating attractors during training of an iterated autoencoder.

Stain transfer using Generative Adversarial Networks and disentangled features.

With the digitization of histopathology, machine learning algorithms have been developed to help pathologists. Color variation in histopathology images degrades the performance of these algorithms. Many models have been proposed to resolve the impact of color variation and transfer histopathology images to a single stain style.

Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition.

Nonlinear components extracted from deep structures of bottleneck neural networks exhibit a great ability to express input space in a low-dimensional manifold. Sharing and combining the components boost the capability of the neural networks to synthesize and interpolate new and imaginary data. This synthesis is possibly a simple model of imaginations in human brain where the components are expressed in a nonlinear low dimensional manifold.

Nonlinear association between serum testosterone levels and coronary artery disease in Iranian men.

Previous studies have shown controversial results about the role of androgens in coronary artery disease (CAD). We performed this study to examine and compare the relationship between androgenic hormones and CAD using conventional linear statistical techniques as well as novel non-linear approaches. The study was conducted on 502 consecutive men who were referred for selective coronary angiography at Tehran Heart Center due to different indications.