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FVR-SGD: A New Flexible Variance-Reduction Method for SGD on Large-Scale Datasets 25th

FVR-SGD: A New Flexible Variance-Reduction Method for SGD on...

引用

25th International conference on neural Information processing (ICONIP)

作者： Tang, Mingxing Huang, Zhen Qiao, Linbo Du, Shuyang Peng, Yuxing Wang, Changjian Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Peoples R China Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9783030041793;9783030041786

Stochastic gradient descent (SGD) is a popular optimization method widely-used in machine learning, while the variance of gradient estimation leads to slow convergence. To accelerate the speed, many variance reduction methods have been proposed. However, most of these methods require additional memory cost or computational burden on full gradient, which results in low efficiency or even unavailable while applied to real-world applications with large-scale datasets. To handle this issue, we propose a new flexible variance reduction method for SGD, named FVR-SGD, which can reduce memory overhead and speedup the convergence using flexible subset size without extra operation. The details of convergence property are presented, the convergence of variance reduction method using flexible subset size can be guaranteed. Several numerical experiments are conducted on a genre of real-world large-scale datasets. The experimental results demonstrated that FVR-SGD outperforms contemporary SVRG algorithm. Specifically, the proposed method can achieve up to 40% reduction in the training time to solve the optimization problem of logistic regression.

关键词： Machine learning Optimization Stochastic gradient descent Variance reduction distributed optimization

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CNN Performance Prediction on a CPU-based Edge Platform 2019

CNN Performance Prediction on a CPU-based Edge Platform

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Proceedings of the 13th International conference on distributed Smart Cameras

作者： D. Velasco-Montero J. Fernández-Berni R. Carmona-Galán Á. Rodríguez-Vázquez Inst. Microelectrónica de Sevilla (IMSE-CNM) CSIC-Univ. Sevilla (Spain)

ISBN: (纸本)9781450371896

The implementation of algorithms based on Deep Learning at edge visual systems is currently a challenge. In addition to accuracy, the network architecture also has an impact on inference performance in terms of throughput and power consumption. This demo showcases per-layer inference performance of various convolutional neural networks running at a low-cost edge platform. Furthermore, an empirical model is applied to predict processing time and power consumption prior to actually running the networks. A comparison between the prediction from our model and the actual inference performance is displayed in real time.

关键词： CPU-based hardware inference performance visual inference deep neural networks Embedded vision system

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LSTM Based network Flow Forecasting in High-Speed Railway Data network

LSTM Based Network Flow Forecasting in High-Speed Railway Da...

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

作者： Yang Zhou Qiang Sun Xiaodong Chen School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9781728152103

network flow forecasting is the basis of network performance analysis and bandwidth allocation. It is of great significance for routing path selection in network service establishment process. The accurate flow forecasting can effectively reduce the service blocking probability. Different forecasting models based on recurrent neural network (RNN) have got extensive attention recently. However, there is relatively less research focusing on network flow forecasting, which is an essential part of future network management. In this paper, we utilize long short term memory (LSTM) model to forecast network flow tendency. To avoid overfitting issue, dropout method is introduced into LSTM. The flow information is collected from High-Speed Railway data network and preprocessed to obtain time-flow series. Two time-flow series are used to verify the network flow forecasting accuracy. Experiments show that the LSTM model can provide accurate forecasting results. The median MAPE of two series forecasting are 0.014 and 0.043, respectively. A shuffled data is used to train the LSTM model too, and corresponding forecasting results indicate that the model has a good generalization ability and it can be applied in different networks.

关键词： bandwidth allocation probability railway communication recurrent neural nets telecommunication computing telecommunication network management telecommunication network routing time series recurrent neural nets bandwidth allocation telecommunication computing Telecommunication network management network flow telecommunication network routing railway communication Probability DADANET Time series high-speed railway

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Approaches for Adaptive Optimization of Routes in a distributed Software-defined Infrastructure of a Virtual Data Center

Approaches for Adaptive Optimization of Routes in a Distribu...

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IEEE conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Bolodurina, Irina P. Parfenov, Denis I. Orenburg State Univ Dept Appl Math Orenburg Russia Orenburg State Univ Fac Distance Learning Technol Orenburg Russia

ISBN: (纸本)9781538643402

In this investigation, we presented a description of the intelligent system for optimization traffic of the cloud applications which processing Big data in network environment. A feature of the developed system is the use of modern virtualization technologies and machine learning methods for managing traffic flows when organizing access to cloud applications and services. The developed solution is based on the hybrid approach. We combining computing powers of CPUs and graphics cards (GPUs) in the analysis and processing of Big data in real time. It allows solving task of placement for network elements and dataset of cloud application in virtual data center. Also we formalize the optimization problem to determine the minimum time for the find minimal cost path for data transfer between storages nodes. This problem is formalized in the form of graph model and it is NP complete. In order to find a suboptimal solution for polynomial time, it is proposed to use the genetic algorithms and neural network approaches.

关键词： multi-cloud platforms software-defined networks software-defined infrastructure virtual data center virtual network functions

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An Efficient Machine Reading Comprehension Method Based on Attention Mechanism

An Efficient Machine Reading Comprehension Method Based on A...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Wenzhen Jin Guocai Yang Hong Zhu Software College Southwest University Chongqing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Machine Reading Comprehension (MRC) answers the questions related to the specified passage to accurately locate the position of the answer. In recent years, many deep learning models based on attention mechanisms have evolved because they provide flexible and effective information interaction. Attention mechanism can further improved by introducing a multi-head self-attention mechanism to obtain the information in different subspaces. In this work, we propose a new model called MHT-Reader by improving multi-head self-attention mechanism with residual network, and convolutional neural network (CNN) combined with highway network to learn the vector representation of passage and question. The experimental results show that on DuReader Search, the BELU-4 is 24.8%, and the Rouge-L is 31.7%, they increased by 1.7% and 2.1% respectively compared with Bidirectional Attention Flow (BiDAF). On DuReader Zhidao, the BELU-4 is 42.1%, and the Rouge-L is 45.6%, they increased by 2.0% and 1.0% respectively compared with BiDAF. Experimental results show that our model has a better performance than BiDAF on the DuReader dataset.

关键词： Semantics Road transportation Feature extraction Residual neural networks Training Software Context modeling

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Cross-Layer Convolutional Siamese network for Visual Tracking 25th

Cross-Layer Convolutional Siamese Network for Visual Trackin...

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25th International conference on neural Information processing (ICONIP)

作者： Chen, Yanyin Chen, Xing Tan, Huibin Zhang, Xiang Lan, Long Huang, Xuhui Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China NUDT HPCL Changsha 410073 Hunan Peoples R China NUDT Coll Comp Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783030041793;9783030041786

In most trackers for visual tracking, Siamese network based trackers construct a pair of twin structures to learn a similarity metric between tracked object and search region to predict the position of the object in the coming frame. They have achieved impressive performance in both speed and accuracy. However, semantic features from different layers are not fully explored in most current Siamese network based tracker. To this, we propose a cross-layer convolutional Siamese network tracker (Siam-CC) which attempts to explore more semantic features of different layers from two aspects. Firstly, we combine the shallow-to-deep cross-layer convolutional response maps to capture various semantic-aware features and meanwhile enforce Siam-CC to only focus on the most interesting location, because much more semantic information is able to reduce negative effect of background. Secondly, to further boost the discrimination of responses, an adaptive contrastive loss is additionally developed together with traditional logistical loss, which, to some extent, assists in filtering out some noisy responses. Experiments on a large-scale benchmark dataset show the effectiveness of Siam-CC as compared to the state-of-the-art trackers.

关键词： Visual tracking Cross-layer convolutional Contrastive loss

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MicroDeep: In-network Deep Learning by Micro-sensor Coordination for Pervasive Computing 4

MicroDeep: In-network Deep Learning by Micro-sensor Coordina...

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4th IEEE International conference on Smart Computing (SMARTCOMP)

作者： Fukushima, Yuta Miura, Daiki Hamatani, Takashi Yamaguchi, Hirozumi Higashino, Teruo Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan

ISBN: (纸本)9781538647059

The conventional wireless sensor networks are designed to gather information from sensors, and pervasive computing systems involve such wireless sensor networks that consist of a number of tiny-/micro-sensors to sense the real-world objects and phenomena. In addition, they also need to conduct training tasks for highly-intelligent processing like pattern recognition and anomaly detection, but doing such tasks in cloud servers may often be impossible or expensive due to lack of communication facility, data transfer overhead, privacy concerns and so on. In this paper, we propose a unique approach to carry out deep learning (both training/testing) among wireless sensors nodes for in-situ, edge-heavy processing. The idea is to appropriately assign neurons of CNN (Convolutional neural network) to wireless nodes, each of which has limited processing capability but can have some power when they are united. For this purpose, we have designed a distributed, restricted version of CNNs. We conducted two experiments using real data;one is for anomaly detection of temperature in an over-1,400m(2) lounge space using 50 temperature sensors to confirm the learning capability as well as communication overhead, and another is for activity recognition using a 6x6 array of thin-, energy-efficient film-type infra-red sensors with micro-processors to demonstrate our concept.

关键词： Edge Computing Sensor network Deep Learning Decentralization

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Sensitive Data Detection Using NN and KNN from Big Data 18th

Sensitive Data Detection Using NN and KNN from Big Data

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18th International conference on Algorithms and Architectures for Parallel processing (ICA3PP)

作者： Adhikari, Binod Kumar Zuo, Wan Li Maharjan, Ramesh Guo, Lin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Tribhuvan Univ Amrit Campus Kathmandu Nepal Changchun Univ Sci & Technol Sch Econ Management Changchun 130012 Peoples R China

ISBN: (纸本)9783030050634;9783030050627

This paper focuses on the determination of sensitive data from huge mass of data collected from social network, cloud drives, local repository files etc. With the advancement of technology, numerous technologies have emerged and are actively being used in extracting useful and critical information about criminal activities from big data that get accumulated due to the use of communicating devices and applications. Numerous reduction techniques and data retrieval algorithm have been invented to extract sensitive information from accumulated data of criminals to prevent future criminal activities and to control unexpected events. In this paper, two different reduction techniques - neural network and K-Nearest Neighbor algorithms are used. Experiments for both algorithms were done in the similar environment by changing data size and node numbers in the processing cluster. From the experiment, it is found that neural network classification algorithm is more superior to retrieve sensitive data from big data than K- nearest neighbor algorithm.

关键词： Terrorism Hadoop distributed File System (HDFS) MapReduce Neurons Cluster

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Exploring memory and time efficient neural networks for image captioning 6th

Exploring memory and time efficient neural networks for imag...

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6th National conference on Computer Vision, Pattern Recognition, Image processing and Graphics, NCVPRIPG 2017

作者： Parameswaran, Sandeep Narayan Visualization and Perception Lab Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)9789811300196

Automatically describing the contents of an image is one of the fundamental problems in artificial intelligence. Recent research has primarily focussed on improving the quality of the generated descriptions. It is possible to construct multiple architectures that achieve equivalent performance for the same task. Among these, the smaller architecture is desirable as they require less communication across servers during distributed training and less bandwidth to export a new model from one place to another through a network. Generally, a deep learning architecture for image captioning consists of a Convolutional neural network (CNN) and a Recurrent neural network (RNN) clubbed together within an encoder-decoder framework. We propose to combine a significantly smaller CNN architecture termed SqueezeNet and a memory and computation efficient LightRNN within a visual attention framework. Experimental evaluation of the proposed architecture on Flickr8k, Flickr30k and MS-COCO datasets reveal superior result when compared to the state of the art. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Recurrent neural networks

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Multi-Step Traffic Flow Prediction Using Recurrent neural network

Multi-Step Traffic Flow Prediction Using Recurrent Neural Ne...

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

作者： Di Yang Huamin Yang Peng Wang Songjiang Li School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Multi-step traffic flow prediction extends short-term single-step prediction to long-term prediction, which is more significant in many basic application in intelligent transportation systems, such as traffic planning. A main problem of multi-step prediction is that the error accumulation as steps increase, resulting in prediction performance degradation. In this work, combining recursive and multi-output strategies, we proposed a deep learning model, named MARNN, for multi-step traffic flow prediction. Specifically, we jointly consider recurrent neural network as dynamic neural network for simulating the dynamic characteristics in traffic time series as recursive strategy does and multi-output strategy for decreasing the accumulated error as step increases. In addition, we introduce attention mechanism for adaptively seeking correlated important information among traffic time series to improve prediction performance. The experiments on real traffic data show the advantages of MARNN model over other four baseline models, demonstrating the potential and promising capability of the proposed model on multi-step traffic flow prediction.

关键词： learning (artificial intelligence) recurrent neural nets road traffic time series traffic engineering computing recurrent neural nets Traffic flow forecasting Road traffic Time series engineering computing Traffic data Travel time Recursive Steps dynamic characteristics Intelligent transportation systems

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