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Demystifying Developers' Issues in Distributed Training of Deep Learning Software [article]

Diandian Gu, Zhenpeng Chen, Yuanqiang Liu, Zili Zhang, Yun Ma, Xin Jin, Xuanzhe Liu
2021 arXiv   pre-print
Deep learning (DL) has been pervasive in a wide spectrum of nowadays software systems and applications.  ...  Given the surging importance of distributed training in the current practice of developing DL software, this paper fills in the knowledge gap and presents the first comprehensive study on developers' issues  ...  INTRODUCTION Deep learning (DL) has been widely adopted in numerous software applications, ranging from supporting daily activities (e.g., speechto-text [28] ) to safety-critical tasks (e.g., autonomous  ... 
arXiv:2112.06222v1 fatcat:kmmqsghqgbdu3btozzysmgy664

Combating The Machine Ethics Crisis: An Educational Approach [article]

Tai Vu
2020 arXiv   pre-print
In recent years, the availability of massive data sets and improved computing power have driven the advent of cutting-edge machine learning algorithms.  ...  In addition, the paper presents several arguments and evidence in favor of the necessity and effectiveness of this integrated approach.  ...  In other words, deep learning is a dark black box, even for capable software engineers and computer scientists.  ... 
arXiv:2004.00817v1 fatcat:gvxa4lcpe5cb3gjnewlawvreqa

Characterizing Performance Bugs in Deep Learning Systems [article]

Junming Cao, Bihuan Chen, Chao Sun, Longjie Hu, Xin Peng
2021 arXiv   pre-print
Deep learning (DL) has been increasingly applied to a variety of domains. The programming paradigm shift from traditional systems to DL systems poses unique challenges in engineering DL systems.  ...  Moreover, we develop a static checker DeepPerf to detect three types of PBs, and identify 488 new PBs in 130 GitHub projects.62 and 18 of them have been respectively confirmed and fixed by developers.  ...  modeling and scalability optimization of distributed deep learning systems.  ... 
arXiv:2112.01771v1 fatcat:dvtxkpzjwzczncrf6wvyei44iu

A Comprehensive Study on Challenges in Deploying Deep Learning Based Software [article]

Zhenpeng Chen and Yanbin Cao and Yuanqiang Liu and Haoyu Wang and Tao Xie and Xuanzhe Liu
2020 arXiv   pre-print
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications.  ...  To help developers of DL software meet the new challenges posed by DL, enormous research efforts in software engineering have been devoted.  ...  INTRODUCTION Deep learning (DL) has been used in a wide range of software applications from different domains, including natural language processing [78] , speech recognition [92] , image processing  ... 
arXiv:2005.00760v4 fatcat:auxizifdrbd6ppahv6ji4cvxs4

Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

Kulvinder Panesar
2020 Journal of Computer-Assisted Linguistic Research  
Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions.  ...  This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the  ...  as syntactic constituency parsing in deep learning.  ... 
doi:10.4995/jclr.2020.12932 fatcat:oogpuyd6zvhixi22k33xawe3dm

Considering the non-programming geographer's perspective when designing extracurricular introductory computer programming workshops

Thomas R Etherington
2018 Journal of Spatial Information Science  
I identify that one of the most important aspects for geographers to learn to computer program is to have training that is domain specific to ensure that the training is relevant and achieves a deeper  ...  learning outcome.  ...  Acknowledgments This work was supported by funding from a Software Sustainability Institute Fellowship, and with logistical support from the Royal Geographical Society (with the Institute of British Geographers  ... 
doi:10.5311/josis.2018.17.442 fatcat:aqtkewudtrcbpaoxrvsohojqcm

Demystifying data and AI for manufacturing: case studies from a major computer maker

Yi-Chun Chen, Bo-Huei He, Shih-Sung Lin, Jonathan Hans Soeseno, Daniel Stanley Tan, Trista Pei-Chun Chen, Wei-Chao Chen
2021 APSIPA Transactions on Signal and Information Processing  
Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches.  ...  The result is a reliable system that can save hundreds of man-years in the qualification process.  ...  We discuss these issues in more detail in the following sections.  ... 
doi:10.1017/atsip.2021.3 fatcat:jxhhsatginfkhlzo4zuuruz3sm

Detailed Technical Programme Schedule

2020 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)  
Specification in Software Development Stages Optimizing trace tool-overhead for lock-intensive threaded parallel applications Twitter Sentiment Analysis During Unlock Period of COVID-19 08.1: Neural Networks  ...  Requirement Elicitation and Specification in Software Development Stages 4 1570668909 Optimizing trace tool multi-threaded parallel applications 5 1570679048 Twitter Sentiment Analysis During  ... 
doi:10.1109/pdgc50313.2020.9315322 fatcat:4ndwytytovb7xkntz7bmaurj6a

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

Xi Chen, Gregory S. Gutmann
2019 The Journal of Computational Science Education  
, and engineers solve real-world problems in a wide range of domains using deep learning and accelerated computing.  ...  DLI's accelerated computing course content starts with the fundamentals of accelerating applications with CUDA and OpenACC in addition to other courses in training and deploying neural networks for deep  ...  ACKNOWLEDGMENTS The authors would like to thank NVIDIA Deep Learning Institute for providing the figures, documents and online course platform, and Tokyo Institute of Technology and ACM University of Kentucky  ... 
doi:10.22369/issn.2153-4136/10/1/16 fatcat:zb5mfkg2aza6dar6gdzj2zfqxq

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program [article]

Xi Chen, Gregory S. Gutmann, Joe Bungo
2018 arXiv   pre-print
, and engineers solve real-world problems in a wide range of domains using deep learning and accelerated computing.  ...  DLI's accelerated computing course content starts with the fundamentals of accelerating applications with CUDA and OpenACC in addition to other courses in training and deploying neural networks for deep  ...  ACKNOWLEDGMENTS The authors would like to thank NVIDIA Deep Learning Institute for providing the figures, documents and online course platform, and Tokyo Institute of Technology and ACM University of Kentucky  ... 
arXiv:1812.08671v3 fatcat:zyjqswy74ves7hywfciohoe7ca

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research [article]

Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Denys Poshyvanyk
2021 arXiv   pre-print
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL).  ...  The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts.  ...  However, developers rated this issue to be the third highest in the amount of effort needed to address it.  ... 
arXiv:2009.06520v2 fatcat:pdhz2265y5hyfglowccmuc4xs4

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [article]

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
2021 arXiv   pre-print
, and privacy leakage in both of the training and inference processes.  ...  However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion  ...  deep neural network (DNN) architectures, customized software and hardware platforms.  ... 
arXiv:2111.12444v1 fatcat:crrbtfylvjeihogumggdnxcbpq

Communication-Efficient Distributed Deep Learning: A Comprehensive Survey [article]

Zhenheng Tang, Shaohuai Shi, Xiaowen Chu, Wei Wang, Bo Li
2020 arXiv   pre-print
Distributed deep learning becomes very common to reduce the overall training time by exploiting multiple computing devices (e.g., GPUs/TPUs) as the size of deep models and data sets increases.  ...  How to address the communication problem in distributed deep learning is becoming a hot research topic recently.  ...  To develop more fault-tolerant algorithms is an important direction to make the training more reliable. Fig. 3 . 3 Overview of distributed deep learning.  ... 
arXiv:2003.06307v1 fatcat:cdkasj4wdvavhgqlxnwj5kd2kq

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Stephanie Chan, Vidhatha Reddy, Bridget Myers, Quinn Thibodeaux, Nicholas Brownstone, Wilson Liao
2020 Dermatology and Therapy  
The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and  ...  Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with  ...  In the second area, deep learning to classify histopathology is also still in its early stages and has underscored the importance of involving dermatologists and dermatopathologists in the development  ... 
doi:10.1007/s13555-020-00372-0 pmid:32253623 fatcat:hq7lmmhnuzcbtioo2jdlwugmci

Advances in Electron Microscopy with Deep Learning

Jeffrey Ede
2020 Zenodo  
This doctoral thesis covers some of my advances in electron microscopy with deep learning.  ...  Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders,  ...  In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a Creative Commons Attribution 4.0 73 license.  ... 
doi:10.5281/zenodo.4399748 fatcat:63ggmnviczg6vlnqugbnrexsgy
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