In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors...

The millimeter-wave frequencies planned for 6G systems present challenges channel modeling. At these frequencies, surface roughness affects wave propagation and causes severe attenuation of (mmWave) signals. In general, beamforming techniques compensate this problem. Analog has some major advantages over its counterpart, digital beamforming, because it uses low-cost phase shifters massive MIMO ...

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's S...

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. They have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we prop...

This report describes the difficulties of training neural networks and in particular deep neural networks. It then provides a literature review of training methods for deep neural networks, with a focus on pre-training. It focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and Stacked Autoencoders and provides an outreach on further and alternative approaches. It also inc...

Deep learning has emerged as a new branch of machine learning in recent years. Some of the related algorithms have been reported to beat state-of-the-art approaches in many applications. The main aim of this paper is to verify one of the deep learning algorithms, specifically a stacked autoencoder, to detect the P300 component. This component, as a specific brain response, is widely used in the...

Deep feature learning has recently emerged with demonstrated effectiveness in domain adaptation. In this paper, we propose a Deep Nonlinear Feature Coding framework (DNFC) for unsupervised domain adaptation. DNFC builds on the marginalized stacked denoising autoencoder (mSDA) to extract rich deep features. We introduce two new elements to mSDA: domain divergence minimization by Maximum Mean Dis...

A hybrid architecture is presented capable of online learning from both labeled and unlabeled samples. It combines both generative and discriminative objectives to derive a new variant of the Deep Belief Network, i.e., the Stacked Boltzmann Experts Network model. The model’s training algorithm is built on principles developed from hybrid discriminative Boltzmann machines and composes deep archi...