Gene regulatory networks (GRNs) consist of gene regulations between transcription factors (TFs) and their target genes. Single-cell RNA sequencing (scRNA-seq) brings both opportunities challenges to the inference GRNs. On one hand, scRNA-seq data reveals statistic information expressions at single-cell resolution, which is conducive construction GRNs; on other noises dropouts pose great difficu...

Graph convolutional neural networks have received a lot of attention in various tasks dealing with graph data by aggregating information from neighboring nodes and passing node information. Many recent studies looked at the impact topological features on classification altering aggregation based degree values or incorporating analysis into networks; however, itself has many characteristics comp...

Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classification tasks, such as sentence classification for Sentiment Analysis or relation extraction. In this work, we study the application of CNNs to language modeling, a dynamic, seque...

This paper proposes a novel memory neural network structure, namely gating recurrent enhanced memory network (GREMN), to model long-range dependency in temporal series on language identification (LID) task at the acoustic frame level. The proposed GREMN is a stacking gating recurrent neural network (RNN) equipped with a learnable enhanced memory block near the classifier. It aims at capturing t...

This paper considers a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Usage of convolutional neural networks (CNN) is the standard approach to image recognition despite the fact they can be too computationally demanding, for example for recognition on mobile platforms or in embedded systems. In this paper we propose C...

Scene labeling is a challenging computer vision task. It requires the use of both local discriminative features and global context information. We adopt a deep recurrent convolutional neural network (RCNN) for this task, which is originally proposed for object recognition. Different from traditional convolutional neural networks (CNN), this model has intra-layer recurrent connections in the con...

This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for the spatial learning in CNN, several reduction steps are taken to form the topi...

In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks. The proposed network, called ReNet, replaces the ubiquitous convolution+pooling layer of the deep convolutional neural network with four recurrent neural networks that sweep horizontally and vertically in both directions across the image. We evaluate the proposed ReNet on thre...

The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the di erences between their features to map them to a multi-dimensional feature space. This paper demonstrates the development of a Convolutional Siamese Network (CSN) for the purpose of case similarity knowledge gener...