نتایج جستجو برای: stacked autoencoder

تعداد نتایج: 12858  

Journal: :CoRR 2015
Zhengping Che Sanjay Purushotham Robinder G. Khemani Yan Liu

Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which...

Journal: :CoRR 2017
Ricardo Gamelas Sousa Jorge M. Santos Luís M. Silva Luís A. Alexandre Tiago Esteves Sara Rocha Paulo Monjardino Joaquim Marques de Sá Francisco Figueiredo Pedro Quelhas

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy (EM). The number of immunogold particles in the c...

The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...

2017
Miao Kang Kefeng Ji Xiangguang Leng Xiangwei Xing Huanxin Zou

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly,...

Journal: :CoRR 2016
Vanika Singhal Shikha Singh Angshul Majumdar

Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the final representation layer is attached to the target to complete the deep neural network. Autoencoders are nested one inside the other to form stacked autoencoders; once th...

2013
Forest Agostinelli Michael R. Anderson Honglak Lee

Stacked sparse denoising autoencoders (SSDAs) have recently been shown to be successful at removing noise from corrupted images. However, like most denoising techniques, the SSDA is not robust to variation in noise types beyond what it has seen during training. To address this limitation, we present the adaptive multi-column stacked sparse denoising autoencoder (AMC-SSDA), a novel technique of ...

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