The molecular mechanisms and functions in complex biological systems currently remain
elusive. Recent high-throughput techniques, such as next-generation sequencing, have …

Unsupervised feature extractors are known to perform an efficient and discriminative
representation of data. Insight into the map**s they perform and human ability to …

A large number of studies have shown that most vehicle collisions are caused by drivers'
abnormal operations. To ensure the safety of all people on the road network as much as …

In this paper, we propose a new driver identification method using deep learning. Existing
driver identification methods have the disadvantages that the size of the sliding time window …

Currently, single fault diagnosis has received mass concern, and the related research
achievements are remarkable. However, because of the mutual interaction of subsystems …

Accurate and reliable crop classification information is a significant data source for
agricultural monitoring and food security evaluation research. It is well-known that …

This paper proposes new techniques for data representation in the context of deep learning
using agglomerative clustering. Existing autoencoder-based data representation techniques …

Deep structures consisting of many layers of nonlinearities have a high potential of
expressing complex relations if properly initialized. Autoencoders play a complementary role …

We propose a new feature representation algorithm using cross-covariance in the context of
deep learning. Existing feature representation algorithms based on the sparse autoencoder …

Autoencoder networks have been demonstrated to be efficient for unsupervised learning of
representation of images, documents and time series. Sparse representation can improve …