Generating High-Quality Face Images from Video with a Multi-Modal Deep Learning Framework – We consider the problem of image categorization in unsupervised scenarios. Two techniques for the task are the use of high-quality labeled labels and a deep learning algorithm. In the experiments on the MNIST benchmark, we show that the algorithm outperforms the state-of-the-art image categorization by a large margin

In this work we present an end-to-end convolutional network for face recognition using RGB images. Our approach uses multiple features for image classification, one shared feature for each feature vector, and a deep attention mechanism to learn features together to train the network. The network learns to predict the feature vectors for each feature vector, as well as to localize them to make more discriminative predictions. We demonstrate the ability to learn a deep attention mechanism (from RGB images) for predicting features from multiple features based on training data. We also demonstrate how a hierarchical attention architecture (one hierarchy of feature descriptors) is used to learn features of multiple feature vectors for this task.

We present a novel method for a naturalistic Bayesian network (BN) model with high-level information, for example, the distribution of objects or of the environment. This is the natural model in general, but not in particular to BN models (such as BN-NN) which operate on high-level information, like the object or the environment. In this paper, we present a novel approach to the BN model from the model’s perspective of high-level information and a model that generalizes naturally in a non-parametric Bayesian setting. The approach is based on a Bayesian Network, where the data are learned from high-level features that are relevant to the model. We show that this Bayesian approach is able to generalize naturally to the model in the domain of high-level observations. We provide computational benchmarks of the methods on a dataset of images in a museum, and show that the generalization ability of the proposed method is superior over other alternatives.

Evaluation of Deep Learning Methods for Accurate Vehicle Speed Matching

A New Paradigm for Recommendation with Friends in Text Messages, On-Line Conversation

Generating High-Quality Face Images from Video with a Multi-Modal Deep Learning Framework

A Study on Word Embeddings in Chinese Word Sense Embeddings

Clustering and Classification with Densely Connected Recurrent Neural NetworksWe present a novel method for a naturalistic Bayesian network (BN) model with high-level information, for example, the distribution of objects or of the environment. This is the natural model in general, but not in particular to BN models (such as BN-NN) which operate on high-level information, like the object or the environment. In this paper, we present a novel approach to the BN model from the model’s perspective of high-level information and a model that generalizes naturally in a non-parametric Bayesian setting. The approach is based on a Bayesian Network, where the data are learned from high-level features that are relevant to the model. We show that this Bayesian approach is able to generalize naturally to the model in the domain of high-level observations. We provide computational benchmarks of the methods on a dataset of images in a museum, and show that the generalization ability of the proposed method is superior over other alternatives.