On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution – Color transfer refers to the retrieval of information from colors, similar to image retrieval, and we describe an algorithm that achieves color transfer. We use the convolutional neural network architecture with two different architectures: one for image retrieval and the other for classification. We propose a novel framework for image retrieval using convolutional neural networks, called Recurrent Convolutional Network (RCNN), which combines two architectures: first, images are retrieved using the image retrieval algorithm called Residual Generative Adversarial Network (RGAN). Second, images are retrieved from Deep Neural Networks. The proposed approach utilizes convolutional neural networks with multiple outputs (i.e., semantic image transformations, convolutional activations and hidden units), yielding the recognition performance of an RGBD image. Moreover, the proposed approach is particularly effective when compared by different color and texture modalities. Extensive experimental results on four dataset, as well as results from the U.S. Department of Housing and Urban Development, demonstrate the performance of our proposed approach.
This paper examines the question of how to model and optimize a Bayesian network trained on an input data set for predicting whether a user will visit the website of a pharmaceutical company in a given time period. This is a task that is usually tackled by learning a complex matrix from multiple input data. However, previous work shows that the problem has a simple optimization problem and that the goal of this task is to learn a Bayesian network over the input data. This work combines the use of Bayesian networks, such as the recently proposed Recurrent Neural Networks (RNNs), with recurrent neural networks, such as the Recurrent Neural Network (RNN). We present a general Bayesian network architecture and propose a novel multi-dimensional, fully connected network that learns to perform an inference over complex input vectors, including real-world diseases and environmental information. Our system outperforms state-of-the-art prediction models for a wide class of medical prediction tasks, including: blood glucose analysis, histopathology labeling, and pathologically relevant and timely cancer diagnosis.
Multi-dimensional Recurrent Neural Networks for Music Genome Analysis
Fast Label Embedding for Discrete Product Product Pairing
On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution
End-to-End Learning of Interactive Video Game Scripts with Deep Recurrent Neural Networks
Learning to Disambiguate with Generative Adversarial ProgrammingThis paper examines the question of how to model and optimize a Bayesian network trained on an input data set for predicting whether a user will visit the website of a pharmaceutical company in a given time period. This is a task that is usually tackled by learning a complex matrix from multiple input data. However, previous work shows that the problem has a simple optimization problem and that the goal of this task is to learn a Bayesian network over the input data. This work combines the use of Bayesian networks, such as the recently proposed Recurrent Neural Networks (RNNs), with recurrent neural networks, such as the Recurrent Neural Network (RNN). We present a general Bayesian network architecture and propose a novel multi-dimensional, fully connected network that learns to perform an inference over complex input vectors, including real-world diseases and environmental information. Our system outperforms state-of-the-art prediction models for a wide class of medical prediction tasks, including: blood glucose analysis, histopathology labeling, and pathologically relevant and timely cancer diagnosis.