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International Conference on Advanced Computer Science and Information Systems

作者： Arif Budiman Mohamad Ivan Fanany Chan Basaruddin Faculty of Computer Science University of Indonesia Depok West Java Indonesia

ISBN: (纸本)9781509003648

Big non-stationary data, which comes in gradual fashion or stream, is one important issue in the application of big data to train deep learning machines. In this paper, we focused on a unique variant of traditional autoencoder, which is called Marginalized Linear Stacked Denoising autoencoder (MLSDA). MLSDA uses a simple linear model. It is faster and uses less number of parameters than the traditional SDA. It also takes advantages of convex optimization. It has better improvement in the bag of words feature representation. However, the traditional SDA with stochastic gradient descent has been more widely accepted in many applications. The stochastic gradient descent is naturally an online learning. It makes the traditional SDA more scalable for streaming big data. This paper proposes a simple modification of MLSDA. Our modification uses matrix multiplication concept for online learning. The experiment result showed the similar accuracy level compared with a batch version of MLSDA and using lower computation resources. The online MLSDA will improve the scalability of MLSDA for handling streaming big data that representing bag of words features for natural language processing, information retrieval, and computer vision.

关键词： autoencoder big data deep learning denoising online sequential

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FACE IDENTIFICATION FROM LOW RESOLUTION NEAR-INFRARED IMAGES

FACE IDENTIFICATION FROM LOW RESOLUTION NEAR-INFRARED IMAGES

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IEEE International Conference on Image Processing

作者： Soumyadeep Ghosh Rohit Keshari Richa Singh Mayank Vatsa IIIT Delhi

ISBN: (纸本)9781467399623

Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes the identification problem even more challenging. We propose an effective method using both hand-crafted and learned features for face identification of low resolution NIR images. We show that learned features contribute considerably to the performance of identification algorithm, and that using both feature level and score level fusion in a hierarchal approach gives good performance. The results demonstrate the effectiveness of the proposed approach on images which are of low quality, low resolution and acquired under challenging illumination conditions in near-infrared mode by surveillance cameras.

关键词： Low resolution face recognition Near Infrared autoencoder Restricted Boltzmann Machine near infrared images Detective cameras Facial Recognition Resolving power low light Nighttime

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A Hybrid Malicious Code Detection Method based on Deep Learning

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2015年第5期9卷 205-215页

作者： Li, Yuancheng Ma, Rong Jiao, Runhai North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

In this paper;we propose a hybrid malicious code detection scheme based on autoencoder and DBN (Deep Belief Networks). Firstly, we use the autoencoder deep learning method to reduce the dimensionality of data. This could convert complicated high-dimensional data into low dimensional codes with the nonlinear mapping, thereby reducing the dimensionality of data, extracting the main features of the data;then using DBN learning method to detect malicious code. DBN is composed of multilayer Restricted Boltzmann Machines (RBM, Restricted Boltzmann Machine) and a layer of BP neural network. Based on unsupervised training of every layer of RBM, we make the output vector of the last layer of RBM as the input vectors of BP neural network, then conduct supervised training to the BP neural network, finally achieve the optimal hybrid model by fine-tuning the entire network. After inputting testing samples into the hybrid model, the experimental results show that the detection accuracy getting by the hybrid detection method proposed in this paper is higher than that of single DBN. The proposed method reduces the time complexity and has better detection performance.

关键词： Malicious code Detection autoencoder DBN RBM deep learning

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Nonparametric guidance of autoencoder representations using label information

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The Journal of Machine Learning Research 2012年第1期13卷

作者： Jasper Snoek Ryan P. Adams Hugo Larochelle Department of Computer Science University of Toronto Toronto ON Canada School of Engineering and Applied Sciences Harvard University Cambridge MA Department of Computer Science University of Sherbrooke Sherbrooke QC Canada

While unsupervised learning has long been useful for density modeling, exploratory data analysis and visualization, it has become increasingly important for discovering features that will later be used for discriminative tasks. Discriminative algorithms often work best with highly-informative features; remarkably, such features can often be learned without the labels. One particularly effective way to perform such unsupervised learning has been to use autoencoder neural networks, which find latent representations that are constrained but nevertheless informative for reconstruction. However, pure unsupervised learning with autoencoders can find representations that may or may not be useful for the ultimate discriminative task. It is a continuing challenge to guide the training of an autoencoder so that it finds features which will be useful for predicting labels. Similarly, we often have a priori information regarding what statistical variation will be irrelevant to the ultimate discriminative task, and we would like to be able to use this for guidance as well. Although a typical strategy would be to include a parametric discriminative model as part of the autoencoder training, here we propose a nonparametric approach that uses a Gaussian process to guide the representation. By using a nonparametric model, we can ensure that a useful discriminative function exists for a given set of features, without explicitly instantiating it. We demonstrate the superiority of this guidance mechanism on four data sets, including a real-world application to rehabilitation research. We also show how our proposed approach can learn to explicitly ignore statistically significant covariate information that is label-irrelevant, by evaluating on the small NORB image recognition problem in which pose and lighting labels are available.

关键词： autoencoder gaussian process gaussian process latent variable model representation learning unsupervised learning

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STOCHASTIC GRADIENT VARIATIONAL BAYES FOR DEEP LEARNING-BASED ASR

STOCHASTIC GRADIENT VARIATIONAL BAYES FOR DEEP LEARNING-BASE...

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IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

作者： Tjandra, Andros Sakti, Sakriani Nakamura, Satoshi Adriani, Mirna Univ Indonesia Fac Comp Sci Java Indonesia Nara Inst Sci & Technol Grad Sch Informat Sci Ikoma Nara Japan

ISBN: (纸本)9781479972913

Many successful methods for training deep neural networks (DNN) rely on an unsupervised pretraining algorithm. It is particularly effective when the number of labeled training samples is not large enough, because pretraining method helps to initialize the parameter values in the appropriate range near a local good minimum, for further discriminative finetuning. However, while the improvement is impressive, training DNN is difficult because the objective function of DNN is highly non-convex function of the parameters. To avoid placing the parameter that generalizes poorly, a robust generative modelling is necessary. This paper explore an alternative of generative modelling for pretraining DNN-based acoustic modelling using Stochastic Gradient Variational Bayes (SGVB) within autoencoder framework called Variational Bayes autoencoder (VBAE). It performs an efficient approximate inference and learning with directed probabilistic graphical models. During fine-tuning, probabilistic encoder parameters with latent variable components are then used in discriminative training for acoustic model. Here, we investigate the performances of DNN-based acoustic model using the proposed pretrained VBAE in comparison with widely used pretraining algorithms like Restricted Boltzmann Machine (RBM) and Stacked Denoising autoencoder (SDAE). The results reveal that VBAE pretraining with Gaussian latent variables gave the best performance.

关键词： acoustic model deep neural network variational bayes autoencoder

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R²FP: Rich and Robust Feature Pooling for Mining Visual Data 15

R<SUP>2</SUP>FP: Rich and Robust Feature Pooling for Mining ...

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IEEE International Conference on Data Mining (ICDM)

作者： Xiong, Wei Du, Bo Zhang, Lefei Hu, Ruimin Bian, Wei Shen, Jialie Tao, Dacheng Wuhan Univ Natl Engn Res Ctr MultimediaSoftware Sch Comp Sci Wuhan Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Sydney NSW 2007 Australia

ISBN: (纸本)9781467395038

The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling ((RFP)-F-2), to better explore rich and robust representation from sparse feature maps of the input data. Both local and global pooling strategies are further considered to instantiate such a method and intensively studied. The former selects the most conductive features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balancing kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed techniques.

关键词： pooling autoencoder representation learning

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Haptics-Enabled Surgical Training System with Guidance Using Deep Learning 9th

Haptics-Enabled Surgical Training System with Guidance Using...

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9th International Conference on Universal Access in Human-Computer Interaction (UAHCI) Held as Part of 17th International Conference on Human-Computer Interaction (HCI)

作者： Biglari, Ehren Feng, Marie Quarles, John Sako, Edward Calhoon, John Rodriguez, Ronald Feng, Yusheng Ctr Simulat Visualizat & Real Time Predict San Antonio TX USA Univ Texas San Antonio Dept Comp Sci San Antonio TX USA Univ Texas San Antonio Dept Cardiothorac Surg San Antonio TX USA Univ Texas Hlth Sci Ctr San Antonio Dept Urol San Antonio TX 78229 USA Univ Texas San Antonio Dept Mech Engn San Antonio TX USA

ISBN: (纸本)9783319206844;9783319206837

In this paper, we present a haptics-enabled surgical training system integrated with deep learning for characterization of particular procedures of experienced surgeons to guide medical residents-in-training with quantifiable patterns. The prototype of virtual reality surgical system is built for open-heart surgery with specific steps and biopsy operation. Two abstract surgical scenarios are designed to emulate incision and biopsy surgical procedures. Using deep learning algorithm (autoencoder), the two surgical procedures were trained and characterized. Results show that a vector with 30 real-valued components can quantify both surgical patterns. These values can be used to compare how a resident-in-training performs differently as opposed to an experienced surgeon so that quantifiable corrective training guidance can be provided.

关键词： Virtual surgical training system Haptic device Machine learning Deep learning algorithm autoencoder Motion tracking and quantification

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Memristor Crossbar Based Unsupervised Training

Memristor Crossbar Based Unsupervised Training

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IEEE National Aerospace and Electronics Conference (NAECON)

作者： Hasan, Raqibul Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9781467375658

Several big data applications are particularly focused on classification and clustering tasks. Robustness of such system depends on how well it can extract important features from the raw data. For big data processing we are interested for a generic feature extraction mechanism for different applications. autoencoder is a popular unsupervised training algorithm for dimensionality reduction and feature extraction. In this work we have examined memiistor crossbar based implementation of autoencoder which will consume very low power. We have designed on-chip training circuitry for the unsupervised training scheme.

关键词： Low power architecture memristor crossbars autoencoder training algorithm feature extraction

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Deep Learning in Exploring Semantic Relatedness for Microblog Dimensionality Reduction

Deep Learning in Exploring Semantic Relatedness for Microblo...

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3rd IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Xu, Lei Jiang, Chunxiao Ren, Yong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781479975914

Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristic, finding proper representations of microblog texts is nontrivial. In this paper, we propose to build deep network-based models to learn low-dimensional representations of microblog texts. The proposed models take advantage of the semantic relatedness derived from two types of microblog-specific information, namely the retweet relationship and hashtags. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.

关键词： microblog mining dimensionality reduction text representation deep learning autoencoder

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Lung Nodule Classification Using Deep Features in CT Images 12

Lung Nodule Classification Using Deep Features in CT Images

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12th Conference on Computer and Robot Vision (CRV)

作者： Kumar, Devinder Wong, Alexander Clausi, David A. Univ Waterloo Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9781479919864

Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for initial diagnosis. Computer-aided diagnosis (CAD) systems can assist radiologists by offering a second opinion and making the whole process faster. We propose a CAD system which uses deep features extracted from an autoencoder to classify lung nodules as either malignant or benign. We use 4303 instances containing 4323 nodules from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy of 75.01% with a sensitivity of 83.35% and false positive of 0.39/patient over a 10 fold cross validation.

关键词： Computer-aided diagnosis (CAD) LIDC deep features autoencoder lung nodule

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