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Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism 21

Training Recommender Systems at Scale: Communication-Efficie...

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27th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Gupta, Vipul Choudhary, Dhruv Tang, Peter Wei, Xiaohan Wang, Xing Huang, Yuzhen Kejariwal, Arun Ramchandran, Kannan Mahoney, Michael W. Univ Calif Berkeley Berkeley CA 94720 USA Facebook Inc Menlo Pk CA USA

ISBN: (纸本)9781450383325

In this paper, we consider hybrid parallelism a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) to scale distributed training of large recommendation models. We propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communicated across the network through a simple hard-thresholding function, allowing only the most relevant information to pass through. For communication efficient DP, DCT compresses the parameter gradients sent to the parameter server during model synchronization. The threshold is updated only once every few thousand iterations to reduce the computational overhead of compression. For communication efficient MP, DCT incorporates a novel technique to compress the activations and gradients sent across the network during the forward and backward propagation, respectively. This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data. We evaluate DCT on publicly available natural language processing and recommender models and datasets, as well as recommendation systems used in production at Facebook. DCT reduces communication by at least 100x and 20x during DP and MP, respectively. The algorithm has been deployed in production, and it improves end-to-end training time for a state-of-the-art industrial recommender model by 37%, without any loss in performance.

关键词： neural networks Recommender systems distributed training Hybrid parallelism

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Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation neural networks

Fault Identification in Honda Scoopy 110CC Continuous Variab...

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IEEE International conference on Communication, networks and Satellite (ComNetSat)

作者： Rizky Dwi Santoso Zein Hanni Pradana Gunawan Wibisono Electrical Engineering Institute Technology Telkom Purwokerto Central Java Indonesia Telecommunications Engineering Institute Technology Telkom Purwokerto Central Java Indonesia

Intensive research in the field of signal processing has driven remarkable advancements in communication technology, particularly in the realm of voice recognition. Voice recognition concepts find application across various domains, with one such application being sound recognition within the context of Continuous Variable Transmission (CVT) for 110cc motor *** study aims to identify potential issues in CVT systems by employing artificial neural networks using Learning Predictive Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), the Artificial neural network (ANN) Backpropagation method to classify distinct sounds emanating from Honda Scoopy 110cc motor scooters. The dataset used comprises 100 CVT engine sound recordings, equally distributed between 50 samples of normal engine sounds and 50 samples of damaged engine *** research findings reveal the highest level of accuracy achieved with order 16 and 16 hidden neurons, resulting in a testing accuracy of 81.3%, a validation accuracy of 100.00%, and a testing accuracy of 90%. This data strongly supports the effectiveness of the backpropagation artificial neural network method for precise CVT issue identification.

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Accelerating Convolutional neural networks via Inter-operator Scheduling

Accelerating Convolutional Neural Networks via Inter-operato...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Yi You Pangfeng Liu Ding-Yong Hong Jan-Jan Wu Wei-Chung Hsu Department of Computer Science and Information Engineering National Taiwan University Graduate Institute of Networking and Multimedia National Taiwan University Graduate School of Advanced Technology National Taiwan University Institute of Information Science Academia Sinica

Convolution neural networks (CNNs) are essential in many machine learning tasks. Current deep learning frameworks and compilers usually treat the neutral network as a DAG (directed acyclic graph) of tensor operations and execute them one at a time according to a topological order, which respects the dependency in the DAG. There are two issues with this general approach. First, new CNNs have branch structures, and they form complex DAGs. These DAGs make it hard to find a good topology sort order that schedules operators within a GPU. Second, modern hardware has high computational power, which makes running operators sequentially on modern hardware under-utilizes resources. These two issues open the possibility of exploiting inter-operator parallelism, i.e., parallelism among independent operators in the DAG, to utilize the hardware resources more efficiently. In this work, we formally define the DAG scheduling problem that addresses the resource contention and propose an early-start-time-first algorithm with two heuristic rules for exploiting parallelism between independent operators. Experimental results show that our method improves the performance by up to 3.76× on RTX 3090 compared to the sequential execution.

关键词： Schedules Tensors Processor scheduling Heuristic algorithms Graphics processing units Parallel processing Hardware

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Temporal Inconsistency-Based Intrinsic Reward for Multi-Agent Reinforcement Learning

Temporal Inconsistency-Based Intrinsic Reward for Multi-Agen...

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International Joint conference on neural networks (IJCNN)

作者： Shaoqi Sun Kele Xu National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China

Multi-agent reinforcement learning (MARL) has shown promising results in many challenging sequential decision-making tasks. Recently, deep neural networks have dominated this field. However, the policy networks of agent's may fall into local optimum during the training phase, which severely constrains the performance of exploration. To address this issue, we propose a novel MARL learning framework named PSAM, which contains a new temporal inconsistency-based intrinsic reward and a diversity control strategy. Specifically, we save the parameters of the deep models along the optimization path of the agent's policy network, which can be denoted as snapshots. Through measuring the difference between snapshots, we can employ the difference as an intrinsic reward. Moreover, we propose a diversity control strategy to improve the performance further. Finally, to verify the effectiveness of the proposed method, we conduct extensive experiments in several widely used MARL environments. The results show that in many environments, PSAM can not only achieve state-of-the-art performance and prevent the policy network from getting stuck in local minima but also accelerate the agent's learning of the policy. It is worth noting that the proposed regularizer can be used using a plug-and-play manner without introducing any additional hyper-parameters and training costs.

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Deep Learning based UAV Assisted distributed Platform for Crowd Source Management

Deep Learning based UAV Assisted Distributed Platform for Cr...

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International conference on Signal processing and Information Security (ICSPIS)

作者： Farman Ullah Abdul Wasay Sardar Mariam Salem Saeed Almeqbaali Meera Saeed Obaid Aldhaheri Salama Obaid Altheeb Al Ali College of Information Technology United Arab Emirates University Al-Ain Abu Dhabi Natural Computing Research & Applications Group and Lero University College of Dublin Ireland

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

Person localization and crowd sourcing in aerial Images is a challenging problem and of great interest in many applications such as crowd safety and security management, people management in disasters, and person movement in an epidemic disease situation. However, the Aerial images have an issue of cluttered background, low-resolution quality, and various environment and lighting conditions, which increase complexity levels of localization and recognition. In this paper, we propose a deep learning-based approach on a Raspberry Pi-powered UAV platform. for the acquisition and localization of crowds considering the different orientations of the UAV and heterogeneous environmental factors. We focus on implementing object location and recognition with various constraints. We collected a large local environment dataset for better accuracy. We compared the performance of Haar-Cascade, template matching, YOLO, and convolutional neural network. The aforementioned results show that YOLO performs better for crowd detection and localization.

关键词： Location awareness YOLO Deep learning Training Accuracy Image resolution Signal processing algorithms Signal processing Autonomous aerial vehicles Testing

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A Novel Deep neural network Model for Credit Risk Prediction of Chinese Farmers 6

A Novel Deep Neural Network Model for Credit Risk Prediction...

引用

6th IEEE International conference on Data Science in Cyberspace, DSC 2021

作者： Xie, Yalong Li, Aiping Liu, Ziniu Chen, Kai Tu, Hongkui College Of Computer National University Of Defense Technology Science And Technology On Parallel And Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665418157

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks. © 2021 IEEE.

关键词： neural network models

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Disturbance Signal Recognition Using Convolutional neural network for DAS System 13

Disturbance Signal Recognition Using Convolutional Neural Ne...

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13th International conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Cheng, Quan Yang, Yin Gui, Xin Wuhan Univ Technol Natl Engn Lab Fiber Opt Sensing Technol Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781665438926

distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

关键词： DAS FBG CNN Signal Recognition

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APT Detection Based on RSDN Framework

APT Detection Based on RSDN Framework

引用

International conference on Electronics Information and Emergency Communication

作者： Mahmoud Basi Tao Shang Jiayu Liu Yatong Jiang Weiwei Wang School of Cyber Science and Technology Beihang University Beijing China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei China College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

Advanced Persistent Threats (APTs) represent sophisticated cyberattacks orchestrated by highly skilled groups, often with support from nation-states or criminal entities. These attacks are meticulously planned and executed against strategic objectives, exhibiting prolonged duration. Identifying and forecasting APTs accurately remains challenging. In this paper, we propose an integrated framework of machine learning models on a big data, referred to as the Random Forest, Support Vector Machine, and Deep neural network (RSDN) System. The scheme collects network traffic data and uploading files. Subsequently, the data undergo processing using Resilient distributed Datasets, employing one-hot encoding techniques, and splitting the datasets into training and testing sets within the spark big data platform. Moreover, we evaluate three different models within the RSDN system and integrate them into scenarios to identify the most effective scheme for detecting APTs. The experimental evaluation demonstrates the effectiveness of our approach in combating APTs, providing valuable insights for cybersecurity practitioners and achieving remarkable accuracy rates of 95% to 98% in their initial phase.

关键词： Support vector machines Training Accuracy Telecommunication traffic Artificial neural networks Big Data Threat assessment

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29th European Signal processing conference, EUSIPCO 2021 - Proceedings

29th European Signal Processing Conference, EUSIPCO 2021 - P...

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29th European Signal processing conference, EUSIPCO 2021

ISBN: (纸本)9789082797060

The proceedings contain 437 papers. The topics discussed include: sampling graph signals with sparse dictionary representation;a penalized subspace strategy for solving large-scale constrained optimization problems;learning multi-layer graphs and a common representation for clustering;performance lower bounds of blind system identification techniques in the presence of channel order estimation error;hierarchical alternating nonlinear least squares for non-negative matrix factorization using rational function;self-supervised complex network for machine sound anomaly detection;PaZoe: classifying time series with few labels;neural network-based acoustic vehicle counting;and evaluation of performance bounds in distributed detection.

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Predicting AGV Battery Cell Voltage Using a neural network Approach with Preliminary Data Analysis and processing

Predicting AGV Battery Cell Voltage Using a Neural Network A...

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IEEE International conference on Big Data

作者： Olena Pavliuk Mykola Medykovskyy Tomasz Steclik Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Automated Control Systems Department Lviv Polytechnic National University Lviv Ukraine

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. Five filters were selected for smoothing: Moving average; Weighted moving average; Exponential smoothing; Smoothing with local regression; Lowess smoothing. RNN forecasting was performed on the basis of a centered stationary signal obtained by subtracting the value obtained with the five filters from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, MBE, SEBIAS, ME. Adequacy of the proposed neural network model based on the determination coefficient R2. The coefficient of determination for all the centered signals obtained using the above five methods yielded adequate predictive models. Exponential smoothing (MAPE=0.002; RMSE=141.481; R2=0.94) and LS (MAPE=0.002; RMSE=124.239; R2:0.96) proved to be the most effective methods for short-term forecasts. Moving average (MAPE=0.00207; RMSE=146.589; R2=0.94) is recommended for long-term forecasts with additional smoothing of the forecasted values because of the noise in them.

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