A New Quantification of Y Chromosome Using Hybridization – We tackle the problem of finding the number of distinct genes of a chromosome through a set of novel and non-sequential binary codes. In this method, a small number of genes is considered, while the rest is considered equally. The task of finding the number of genes of a chromosome is a fundamental problem, and the results of this particular task have been extensively studied.

We present a methodology to automatically predict a classifier’s ability to represent data. This can be seen as the first step in the development of a new paradigm for automated classification of complex data. This approach is based on learning a deep representation that learns to recognize the natural feature (like class labels) of the data. We propose a novel classifier called the Convolutional Neural Network (CNN) for recognizing natural features in this context: the data is composed of latent variables and a classifier can learn a network from this latent variable. We also propose a model that does not require a prior distribution over the latent variables. This can be seen as a non-trivial and challenging task, since it requires two-to-one labels for each latent variable. We propose a general framework that is applicable to different data sources. Our framework is based on Deep Convolutional Nets for Natural-Face Modeling (DCNNs) and is fully automatic. This study is a part of an additional contribution in this area.

Convolutional Convolutional Neural Networks for Brain Lesions Detection

Learning from the Fallen: Deep Cross Domain Embedding

A New Quantification of Y Chromosome Using Hybridization

Provenance-relaxed feature selection for semi-supervised deep networks

Learning Deep ClassifiersWe present a methodology to automatically predict a classifier’s ability to represent data. This can be seen as the first step in the development of a new paradigm for automated classification of complex data. This approach is based on learning a deep representation that learns to recognize the natural feature (like class labels) of the data. We propose a novel classifier called the Convolutional Neural Network (CNN) for recognizing natural features in this context: the data is composed of latent variables and a classifier can learn a network from this latent variable. We also propose a model that does not require a prior distribution over the latent variables. This can be seen as a non-trivial and challenging task, since it requires two-to-one labels for each latent variable. We propose a general framework that is applicable to different data sources. Our framework is based on Deep Convolutional Nets for Natural-Face Modeling (DCNNs) and is fully automatic. This study is a part of an additional contribution in this area.