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Application Research of Deep Learning in Natural Language processing

Application Research of Deep Learning in Natural Language Pr...

引用

2019 2nd International conference on Computer Science and Advanced Materials(CSAM 2019)

作者： Junmin Zhu Yiang Gao Cangzhou Bank Tsien Hsue-Shen College Nanjing University of Science &Technology

With the development of deep learning and its application in related fields, the performance of machine learning has been significantly improved. The studies of deep learning applied in natural language processing tasks has made many breakthroughs. This paper first introduces the basic concepts of deep learning from the perspective of its structure and application motivation, then focuses on the analysis of the neural network language model, using distributed representation to remove dimension disasters. Finally, this paper introduces the typical applications of deep learning in natural language processing, including syntactic analysis, word meaning learning and sentiment analysis to provide some references for the relevant researchers.

关键词： Deep learning Natural language processing neural network

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The Effect of network Width on the Performance of Large-batch Training 32

The Effect of Network Width on the Performance of Large-batc...

引用

32nd conference on neural Information processing Systems (NIPS)

作者： Chen, Lingjiao Wang, Hongyi Zhao, Jinman Koutris, Paraschos Papailiopoulos, Dimitris Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Elect & Comp Engn 1415 Johnson Dr Madison WI 53706 USA

distributed implementations of mini-batch stochastic gradient descent (SGD) suffer from communication overheads, attributed to the high frequency of gradient updates inherent in small-batch training. Training with large batches can reduce these overheads;however it besets the convergence of the algorithm and the generalization performance. In this work, we take a first step towards analyzing how the structure (width and depth) of a neural network affects the performance of large-batch training. We present new theoretical results which suggest that-for a fixed number of parameters-wider networks are more amenable to fast large-batch training compared to deeper ones. We provide extensive experiments on residual and fully-connected neural networks which suggest that wider networks can be trained using larger batches without incurring a convergence slow-down, unlike their deeper variants.

关键词： Stochastic systems

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Low Precision Deep Learning Training on Mobile Heterogeneous Platform

Low Precision Deep Learning Training on Mobile Heterogeneous...

引用

26th Euromicro International conference on Parallel, distributed, and network-Based processing (PDP)

作者： Valery, Olivier Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781538649756

Recent advances in System-on-Chip architectures have made the use of deep learning suitable for a number of applications on mobile devices. Unfortunately, due to the computational cost of neural network training, it is often limited to inference task, e.g., prediction, on mobile devices. In this paper, we propose a deep learning framework that enables both deep learning training and inference tasks on mobile devices. While being able to accommodate with the heterogeneity of computing devices technology on mobile devices, it also uses OpenCL to efficiently leverage modern SoC capabilities, e.g., multi-core CPU, integrated GPU and shared memory architecture, and accelerate deep learning computation. In addition, our system encodes the arithmetic operations of deep networks down to 8-bit fixed-point on mobile devices. As a proof of concept, we trained three well-known neural networks on mobile devices and exhibited a significant performance gain, energy consumption reduction, and memory saving.

关键词： OpenCL GPGPU mobile computing heterogeneous system System-on-Chip deep learning transfer learning

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distributed algorithm for solving linear algebraic equations: An implicit gradient neural network approach

Distributed algorithm for solving linear algebraic equations...

引用

Control Technology and Applications (CCTA),

作者： Peijun Wang Wenwu Yu Guanghui Wen School of Mathematics Southeast University Nanjing China

This brief investigates the online solving problem for linear algebraic equation Ax = b by means of the principle of consensus in multi-agent systems, where A ∈ ℝ m×n and b ∈ Rn. To be specific, we choose m autonomous agents and agent i knows only the i-th row of [A b] under a fixed and connected undirected communication topology. Under local interactions, by designing an implicit gradient neural network based algorithm, it is shown that all the agents' states which starting from any different initial conditions can converge exponentially fast to one of the solutions to Ax = b, if the matrix A has full row rank. It is worth noting that the proposed algorithm is fully distributed. In addition, it is shown that the proposed algorithm is effective in obtaining least square solutions for no-solution cases. Finally, computer simulations verify and demonstrate the efficiency of the proposed methods for solving linear algebraic equations.

关键词： Mathematical model neural networks Signal processing algorithms Convergence distributed algorithms Heuristic algorithms Iterative methods

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Improved Fast Stereo Matching Algorithm Based on Convolutional neural networks

Improved Fast Stereo Matching Algorithm Based on Convolution...

引用

IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Chunfeng Xu Shiyu Lu Wen Liu Ye Bai Cheng Han School of Computer Science and Technology Changchun University of Science and Technology Changchun China College of Optical and Electronical Information Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Various stereo matching algorithms are widely used in many fields nowadays. In order to solve low speed problem in convolutional neural network for stereo matching algorithms (MC-CNN-fst), an improved stereo matching algorithm based on convolutional neural network was proposed. By modifying pooling layers and normalization layer, the structure of the convolutional neural network has been reformed. Among them, different pooling layers are introduced to retain more image information, and the normalization layer is introduced to accelerate the speed of convergence of training, and finally the disparity map is obtained through subsequent processing steps. To evaluate the performance of the proposed algorithm and the original one under the same experimental environment. We conduct the experiment on two public datasets: KITTI and Middlebury. Experimental results demonstrate that the refinement of the network structure boosts the speed for the feature extraction procedure by 50%, and the total operation time is reduced by 12.5%.

关键词： convolutional neural nets feature extraction image matching stereo image processing Stereo image processing Feature extraction image matching neural network operation time algorithms image information normalization network STRUCTURE

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TRAINING DEEP neural networkS VIA OPTIMIZATION OVER GRAPHS

TRAINING DEEP NEURAL NETWORKS VIA OPTIMIZATION OVER GRAPHS

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhang, Guoqiang Kleijn, W. Bastiaan Univ Technol Sydney Sch Elect & Data Engn Ctr Audio Acoust & Vibrat Sydney NSW Australia Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781538646588

In this work, we propose to train a deep neural network by distributed optimization over a graph. Two nonlinear functions are considered: the rectified linear unit (ReLU) and a linear unit with both lower and upper cutoffs (DCutLU). The problem reformulation over a graph is realized by explicitly representing ReLU or DCutLU using a set of slack variables. We then apply the alternating direction method of multipliers (ADMM) to update the weights of the network layer-wise by solving subproblems of the reformulated problem. Empirical results suggest that the ADMM-based method is less sensitive to overfitting than the stochastic gradient descent (SGD) and Adam methods.

关键词： Deep learning DNN optimization ADMM

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ShmCaffe: A distributed Deep Learning Platform with Shared Memory Buffer for HPC Architecture 38

ShmCaffe: A Distributed Deep Learning Platform with Shared M...

引用

38th IEEE International conference on distributed Computing Systems (ICDCS)

作者： Ahn, Shinyoung Kim, Joongheon Lim, Eunji Choi, Wan Mohaisen, Aziz Kang, Sungwon Korea Adv Inst Sci & Technol KAIST Dept Info & Comm Engn Daejeon South Korea Elect & Telecommun Res Inst ETRI High Performance Comp Grp Daejeon South Korea Chung Ang Univ Sch Comp Sci & Engn Seoul South Korea Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Korea Adv Inst Sci & Technol KAIST Sch Comp Daejeon South Korea

ISBN: (纸本)9781538668719

One of the reasons behind the tremendous success of deep learning theory and applications in the recent days is advances in distributed and parallel high performance computing (HPC). This paper proposes a new distributed deep learning platform, named ShmCaffe, which utilizes remote shared memory for communication overhead reduction in massive deep neural network training parameter sharing. ShmCaffe is designed based on Soft Memory Box (SMB), a virtual shared memory framework. In the SMB framework, the remote shared memory is used as a shared buffer for asynchronous massive parameter sharing among many distributed deep learning processes. Moreover, a hybrid method that combines asynchronous and synchronous parameter sharing methods is also discussed in this paper for improving scalability. As a result, ShmCaffe is 10.1 times faster than Caffe and 2.8 times faster than Caffe-MPI for deep neural network training when Inception_v1 is trained with 16 GPUs. We verify the convergence of the Inception_v1 model training using ShmCaffe-A and ShmCaffe-H by varying the number of workers. Furthermore, we evaluate scalability of ShmCaffe by analyzing the computation and communication times per one iteration of deep learning training in four convolutional neural network (CNN) models.

关键词： Machine learning Training Servers Computational modeling Parallel processing Data models Computer architecture

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Pseudorehearsal in actor-critic agents with neural network function approximation 32

Pseudorehearsal in actor-critic agents with neural network f...

引用

32nd IEEE International conference on Advanced Information networking and Applications (AINA)

作者： Marochko, Vladimir Johard, Leonard Mazzara, Manuel Longo, Luca LLC Innodata Innopolis Russia Innopolis Univ Innopolis Russia Dublin Inst Technol Dublin Ireland

ISBN: (纸本)9781538621950

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.

关键词： reinforcement learning neural networks catastrophic forgetting pseudorehearsal

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Unconsciousness Detection Supervision System Using Faster RCNN Architecture 18

Unconsciousness Detection Supervision System Using Faster RC...

引用

2nd International conference on Future networks and distributed Systems (ICFNDS)

作者： Abedi, Wafaa M. Salih

ISBN: (纸本)9781450364287

Unconsciousness is a state which takes place when the person is unable to maintain his/her awareness of the surroundings. It is a fully or nearly loss of responsiveness to environmental stimuli. As a consequence of that state, physical injuries, and or mental problems might take place. Hence, this paper proposes a simple faint detection technique using Faster Region-based Convolutional neural network (Faster RCNN) with thermal imaging camera to observe the unconscious person or a fainted one, and consequently an instant and convenient treatment will be carried out to that person. Experimental results reveal that the proposed architecture achieved a precise accuracy during the dim lightening condition, and a fairly excellent result indoor in ascertaining where the case is.

关键词： Image processing unconsciousness detection Algorithm detection Algorithms Convolutional neural network Faster RCNN

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distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization 32

Distributed Learning without Distress: Privacy-Preserving Em...

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32nd conference on neural Information processing Systems (NIPS)

作者： Jayaraman, Bargav Wang, Lingxiao Evans, David Gu, Quanquan Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

distributed learning allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. We present a distributed learning approach that combines differential privacy with secure multiparty computation. We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting. In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model. In our gradient perturbation method, the data owners collaboratively train a global model via an iterative learning algorithm. At each iteration, the parties aggregate their local gradients within a secure computation, adding sufficient noise to ensure privacy before the gradient updates are revealed. For both methods, we show that the noise can be reduced in the multi-party setting by adding the noise inside the secure computation after aggregation, asymptotically improving upon the best previous results. Experiments on real world data sets demonstrate that our methods provide substantial utility gains for typical privacy requirements.

关键词： Iterative methods

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