Video Game Character Generation with Multiword Modulo Theories – The problem of word embedding, with its implications for the research of computer vision and statistics, has become essential to the evolution of our society. One of the main challenges with word embedding (embedding) methods is to model word embeddings. In this paper we report an application of embedding to the task of word identification by encoding a series of tokens from a corpus into a single vector vector representation. In contrast to previous work on word embeddings we propose an embedding approach that learns to represent words by the word embedding in its vectors, using a novel concept of the word entity. The proposed method is demonstrated to outperform the state of the art word embeddings on two separate tasks, including word identification and language recognition.

In this paper we propose a novel approach to face reconstruction using the multi-task multi-layer CNN approach. This method is based on using the CNN architecture for face reconstruction. To ensure accurate reconstruction of the whole face, we employ multilayer perceptron (ML) networks for face reconstruction. With the ML network, the whole face is reconstructed by one layer of CNN architecture. To deal with the large number of features in the ML network, we also use the CNN architecture to reconstruct the entire face. We evaluate the ability to generate discriminative features for a given face using the MNIST dataset (and the CNN model).

Supervised Hierarchical Clustering Using Transformed LSTM Networks

Learning to Detect Small Signs from Large Images

Video Game Character Generation with Multiword Modulo Theories

Learning Disentangled Representations with Latent Factor Modeling

Facial-torture reconstruction with deep convolutional autoencodersIn this paper we propose a novel approach to face reconstruction using the multi-task multi-layer CNN approach. This method is based on using the CNN architecture for face reconstruction. To ensure accurate reconstruction of the whole face, we employ multilayer perceptron (ML) networks for face reconstruction. With the ML network, the whole face is reconstructed by one layer of CNN architecture. To deal with the large number of features in the ML network, we also use the CNN architecture to reconstruct the entire face. We evaluate the ability to generate discriminative features for a given face using the MNIST dataset (and the CNN model).