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An Overview of Neural Network Compression [article]

James O' Neill
2020 arXiv   pre-print
Hence, this paper provides a timely overview of both old and current compression techniques for deep neural networks, including pruning, quantization, tensor decomposition, knowledge distillation and combinations  ...  We assume a basic familiarity with deep learning architectures[%s], namely, Recurrent Neural Networks , Convolutional Neural Networks [%s] and Self-Attention based networks [%s],[%s].  ...  The resulting pruned network is evaluated without fine-tuning, avoiding retraining to improve computational cost and time.  ... 
arXiv:2006.03669v2 fatcat:u2p6gvwhobh53hfjxawzclw7fq

2020 Index IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Vol. 39

2020 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
Self-Learning and Efficient Health-Status Analysis for a Core Router System.  ...  ,Wang, Z., Shi, Y., and Hu, J., Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning; 3445-3457 Wu, Y.  ...  ., +, TCAD Oct. 2020 2588 -2601 FLASH: Fast, Parallel, and Accurate Simulator for HLS. Choi, Y., +, TCAD Dec. 2020 4828-4841 High-Level Synthesis Design Space Exploration: Past, Present, and Future.  ... 
doi:10.1109/tcad.2021.3054536 fatcat:wsw3olpxzbeclenhex3f73qlw4

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review [article]

Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette
2021 arXiv   pre-print
We analyzed the main trends and problems of each sub-field and provided summaries of the papers extracted.  ...  Context: Machine Learning (ML) has been at the heart of many innovations over the past years.  ...  Many thanks also goes to Freddy Lécué from Thalès, who provided us feedback on an early version of this manuscript. They all contributed to improving this SLR.  ... 
arXiv:2107.12045v3 fatcat:43vqxywawbeflhs6ehzovvsevm

Efficient Training and Compression of Deep Neural Networks

James O' Neill
typical resources available to the majority of machine learning practitioners.  ...  The application of deep neural networks is widespread throughout the world and is responsible for many crucial applications such as self-driving cars, machine translation, spoken language recognition,  ...  self-distillation and standard fine-tuned models.  ... 
doi:10.17638/03157802 fatcat:kboe4vvizfcyhlbdpx6lw7cnzm

The Hebrew University of Jerusalem 1918–60

Edwin Samuel
1962 International Affairs  
The learning complexity is linear in the number of model parts and image features, compared to the exponential complexity of traditional methods for relational model learning.  ...  In the first stage a model of the basic category is learned, and it is used to form a part-based vector representation for images.  ...  Acknowledgements To be inserted. ii Acknowledgements We are grateful to Tomer Hertz, which was involved in early stages of this research for his support and his help in the empirical validation of the  ... 
doi:10.2307/2610458 fatcat:gzlzysbzavdhppdh6eshsl7y3q

30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts

2021 Journal of Computational Neuroscience  
One of the goals of neuroscience is to understand the computational principles that describe the formation of behaviorally relevant signals in the brain, as well as how these computations are realized  ...  within the constraints of biological networks.  ...  Owing to the randomness of positions of cells and connectivity, there were large variations of the strength of the PC response depending on the precise location of the stimulus.  ... 
doi:10.1007/s10827-021-00801-9 pmid:34931275 pmcid:PMC8687879 fatcat:evpmmfpaivgpxdqpive5xdgmwu

Time Series Analysis and Modeling to Forecast: a Survey [article]

Fatoumata Dama, Christine Sinoquet
2021 arXiv   pre-print
Time series modeling for predictive purpose has been an active research area of machine learning for many years.  ...  We describe three major linear parametric models, together with two nonlinear extensions, and present five categories of nonlinear parametric models.  ...  Further, the authors proposed a log-sparse self-attention mechanism, to increase forecasting accuracy for time series with fine granularity and long-term dependencies under memory-limited budget.  ... 
arXiv:2104.00164v2 fatcat:zz7kaefskrhvrl7wkmbgcbkkfu

Foreword of Special Issue on "E-Service and Applications"

Jason C. Hung, Hsing-I Wang
2011 Journal of Computers  
deployment configuration, Web services, Vision on novel advances of automated intelligent business agents and e-Commerce, Modeling and simulation of business processes, e-Supply Chains, e-Logistics, e-Procurement  ...  In this paper, the weight of each criterion in the AHP tree is calculated and examples of demonstrating how the indicators are applied to the real cases to determine the performance of the strategies for  ...  Ranga Naras, the Dean of Graduate School of University of Northern Virginia, who is also the first author's doctoral dissertation research supervisor.  ... 
doi:10.4304/jcp.6.3.387-388 fatcat:6uf2oayvbbfibphaobqewpnkx4

ijair-volume-6-issue-1-vii-january-march-2019 -HINDUSTAN BOOK.pdf

V. Thamilarasi
(CAD), picture archiving and communication system (PACS), and for training and testing.  ...  Self Organizing Map The self Organizing Map (SOM) is a classification of unsupervised learning from the type of Partitive clustering. It is used for visualization and high dimensional datasets.  ...  This involves the use of strong encryption techniques for data security and fine-grained authorization to control access to data.  ... 
doi:10.6084/m9.figshare.20217722.v1 fatcat:l74ihuqhcvdtjomod3zdwzfniu

ACNP 57th Annual Meeting: Poster Session II

2018 Neuropsychopharmacology  
Increasing evidence suggests a role for systemic and neurological inflammation in the pathophysiology of fear and trauma exposure based psychiatric disorders (Micholpoulos et al., 2017; Haroon et al.,  ...  A composite avoidance score (average of Z-scores across open field, light-dark box, and trauma reminder) demonstrated a main effect of increased avoidance behaviors by two-way ANOVA (Fstress = 9.10, p  ...  Keywords: Reinforcement Learning, Prefrontal Cortex, Explore-Exploit Dilemma, Probabilistic Reward Learning, Computational Models Of Decision-Making Disclosure: Nothing to disclose. T22.  ... 
doi:10.1038/s41386-018-0267-6 fatcat:febeq6uwefgdzm65ccmzrmjwjy

Approximate Inference: New Visions

Yingzhen Li, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository, Richard Eric Turner
Critically, the success of Bayesian methods in practice, including the recent resurgence of Bayesian deep learning, relies on fast and accurate approximate Bayesian inference applied to probabilistic models  ...  Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life.  ...  Variational inference might be preferred in this regard, as under some assumptions, the PAC-Bayes framework provides generalisation bounds on future observations (coming from the same underlying distribution  ... 
doi:10.17863/cam.24865 fatcat:weiquqeeejbmdb3zyccd3j4vpy

Affine–invariant texture analysis and retrieval of 3D medical images with clinical context integration

Adrien Depeursinge, Christian Pellegrini, Antoine Geissbuhler, Henning Muller
Two approaches are studied: a probabilistic one with the naive Bayes classifier and a geometric one with k-NN.  ...  When required, the user can manually edit the lung mask. The lung mask can be visualized in 2D and 3D to verify the completeness of the segmented volume.  ...  C pruning is the feature confidence factor for pruning the tree in C4.  ... 
doi:10.13097/archive-ouverte/unige:6550 fatcat:wiii34xlwjhuhciqixoj7z5lam

CARS 2016—Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition Heidelberg, Germany, June 21–25, 2016

2016 International Journal of Computer Assisted Radiology and Surgery  
Acknowledgments The authors wish to thank Fundación CEIBA and Alcaldía Mayor de Bogotá, for the financial support of Ricardo Mendoza's PhD studies through the scholarship program ''Becas Rodolfo Llinás  ...  '', and Amazon Inc., for providing valuable computing resources through an ''AWS in Education Research'' grant.  ...  to attempt to further fine-tune the image acquisition process in CBCT for a specific diagnostic task.  ... 
doi:10.1007/s11548-016-1412-5 pmid:27206418 fatcat:uk5r46n2xvhedkfjzmeiweyneq

Large-scale semi-supervised learning for natural language processing

Shane A Bergsma
This combination of learning from both labeled and unlabeled data is often referred to as semi-supervised learning.  ...  By automatically labeling a large number of examples, we can train powerful discriminative models, leveraging fine-grained features of input words.  ...  Blum and Mitchell [1998] give a PAC Learning-style framework for this approach, and give empirical results on the web-page classification task.  ... 
doi:10.7939/r31d1p fatcat:dhnctx46bzfeznfxloxqdue5xq

Chasing the AIDS virus

Thomas Lengauer, André Altmann, Alexander Thielen, Rolf Kaiser
2010 Communications of the ACM  
ACM Media Advertising Policy Communications of the ACM and other ACM Media publications accept advertising in both print and electronic formats.  ...  All advertising in ACM Media publications is at the discretion of ACM and is intended to provide financial support for the various activities and services for ACM members.  ...  All errors and omissions are my own (though of course I faced constraints on length and number of citations).  ... 
doi:10.1145/1666420.1666440 fatcat:o2qllqh4tzhh5dzgvnjewl52vq
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