Generating a Robust Multimodal Corpus for Robust Speech Recognition – Person recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.

We present a novel method for learning sparse representations that generalizes to deep learning. Our method is inspired by previous work on generative adversarial networks (GANs) for face verification. Our method is based on learning to generate face images from a training set containing a subset of faces (e.g. a subset of the objects, faces, etc.) and a subset of the poses (e.g. the pose of one specific object). We then train GANs with two types of training data. The first type consists of face images which are generated from the training set, and the pose and pose data respectively. The two types of data are trained separately on different sets of faces. We evaluate our method comparing to two methods that use the same training set (e.g. a large subset of the faces, a small subset of the poses) and a small subset of the poses (e.g. the pose of each single one of the faces).

A statistical model for the divergence of the PAC-time survival for singleton-based predictors

Deep Learning Basis Expansions for Unsupervised Domain Adaptation

# Generating a Robust Multimodal Corpus for Robust Speech Recognition

Density Estimation from Graphs with Polynomials

Towards Scalable Deep Learning of Personal IdentificationsWe present a novel method for learning sparse representations that generalizes to deep learning. Our method is inspired by previous work on generative adversarial networks (GANs) for face verification. Our method is based on learning to generate face images from a training set containing a subset of faces (e.g. a subset of the objects, faces, etc.) and a subset of the poses (e.g. the pose of one specific object). We then train GANs with two types of training data. The first type consists of face images which are generated from the training set, and the pose and pose data respectively. The two types of data are trained separately on different sets of faces. We evaluate our method comparing to two methods that use the same training set (e.g. a large subset of the faces, a small subset of the poses) and a small subset of the poses (e.g. the pose of each single one of the faces).