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Rozprawy doktorskie na temat „Approximate identity neural networks”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 1 lutego 2022

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1

Ling, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.

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Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in

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Garces, Freddy. "Dynamic neural networks for approximate input- output linearisation-decoupling of dynamic systems." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368662.

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Li, Yingzhen. "Approximate inference : new visions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277549.

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Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these systems is to perform automated decision making, and in order to achieve this, predictive systems need to return estimates of their confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable

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Liu, Leo M. Eng Massachusetts Institute of Technology. "Acoustic models for speech recognition using Deep Neural Networks based on approximate math." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100633.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 81-83).<br>Deep Neural Networks (DNNs) are eective models for machine learning. Unfortunately, training a DNN is extremely time-consuming, even with the aid of a graphics processing unit (GPU). DNN training is especially slo

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Scotti, Andrea. "Graph Neural Networks and Learned Approximate Message Passing Algorithms for Massive MIMO Detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284500.

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Massive multiple-input and multiple-output (MIMO) is a method to improvethe performance of wireless communication systems by having a large numberof antennas at both the transmitter and the receiver. In the fifth-generation(5G) mobile communication system, Massive MIMO is a key technology toface the increasing number of mobile users and satisfy user demands. At thesame time, recovering the transmitted information in a massive MIMO uplinkreceiver requires more computational complexity when the number of transmittersincreases. Indeed, the optimal maximum likelihood (ML) detector hasa complexity

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Gaur, Yamini. "Exploring Per-Input Filter Selection and Approximation Techniques for Deep Neural Networks." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90404.

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We propose a dynamic, input dependent filter approximation and selection technique to improve the computational efficiency of Deep Neural Networks. The approximation techniques convert 32 bit floating point representation of filter weights in neural networks into smaller precision values. This is done by reducing the number of bits used to represent the weights. In order to calculate the per-input error between the trained full precision filter weights and the approximated weights, a metric called Multiplication Error (ME) has been chosen. For convolutional layers, ME is calculated by subtract

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Dumlupinar, Taha. "Approximate Analysis And Condition Assesment Of Reinforced Concrete T-beam Bridges Using Artificial Neural Networks." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609732/index.pdf.

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In recent years, artificial neural networks (ANNs) have been employed for estimation and prediction purposes in many areas of civil/structural engineering. In this thesis, multilayered feedforward backpropagation algorithm is used for the approximate analysis and calibration of RC T-beam bridges and modeling of bridge ratings of these bridges. Currently bridges are analyzed using a standard FEM program. However, when a large population of bridges is concerned, such as the one considered in this project (Pennsylvania T-beam bridge population), it is impractical to carry out FEM analysis of all

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8

Tornstad, Magnus. "Evaluating the Practicality of Using a Kronecker-Factored Approximate Curvature Matrix in Newton's Method for Optimization in Neural Networks." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275741.

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For a long time, second-order optimization methods have been regarded as computationally inefficient and intractable for solving the optimization problem associated with deep learning. However, proposed in recent research is an adaptation of Newton's method for optimization in which the Hessian is approximated by a Kronecker-factored approximate curvature matrix, known as KFAC. This work aims to assess its practicality for use in deep learning. Benchmarks were performed using abstract, binary, classification problems, as well as the real-world Boston Housing regression problem, and both deep a

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9

Hanselmann, Thomas. "Approximate dynamic programming with adaptive critics and the algebraic perceptron as a fast neural network related to support vector machines." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0005.

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[Truncated abstract. Please see the pdf version for the complete text. Also, formulae and special characters can only be approximated here. Please see the pdf version of this abstract for an accurate reproduction.] This thesis treats two aspects of intelligent control: The first part is about long-term optimization by approximating dynamic programming and in the second part a specific class of a fast neural network, related to support vector machines (SVMs), is considered. The first part relates to approximate dynamic programming, especially in the framework of adaptive critic designs (ACDs

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Malfatti, Guilherme Meneguzzi. "Técnicas de agrupamento de dados para computação aproximativa." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169096.

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Dois dos principais fatores do aumento da performance em aplicações single-thread – frequência de operação e exploração do paralelismo no nível das instruções – tiveram pouco avanço nos últimos anos devido a restrições de potência. Neste contexto, considerando a natureza tolerante a imprecisões (i.e.: suas saídas podem conter um nível aceitável de ruído sem comprometer o resultado final) de muitas aplicações atuais, como processamento de imagens e aprendizado de máquina, a computação aproximativa torna-se uma abordagem atrativa. Esta técnica baseia-se em computar valores aproximados ao invés d

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11

Romano, Michele. "Near real-time detection and approximate location of pipe bursts and other events in water distribution systems." Thesis, University of Exeter, 2012. http://hdl.handle.net/10871/9862.

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The research work presented in this thesis describes the development and testing of a new data analysis methodology for the automated near real-time detection and approximate location of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions, equipment failures, etc.) in Water Distribution Systems (WDSs). This methodology makes synergistic use of several self-learning Artificial Intelligence (AI) and statistical/geostatistical techniques for the analysis of the stream of data (i.e., signals) collected and communicated on-line by the

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12

Gómez, Cerdà Vicenç. "Algorithms and complex phenomena in networks: Neural ensembles, statistical, interference and online communities." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7548.

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Aquesta tesi tracta d'algoritmes i fenòmens complexos en xarxes.<br/><br/>En la primera part s'estudia un model de neurones estocàstiques inter-comunicades mitjançant potencials d'acció. Proposem una tècnica de modelització a escala mesoscòpica i estudiem una transició de fase en un acoblament crític entre les neurones. Derivem una regla de plasticitat sinàptica local que fa que la xarxa s'auto-organitzi en el punt crític.<br/><br/>Seguidament tractem el problema d'inferència aproximada en xarxes probabilístiques mitjançant un algorisme que corregeix la solució obtinguda via belief propagation

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13

Glaros, Anastasios. "Data-driven Definition of Cell Types Based on Single-cell Gene Expression Data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297498.

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14

Matula, Tomáš. "Využití aproximovaných aritmetických obvodů v neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399179.

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Táto práca sa zaoberá využitím aproximovaných obvodov v neurónových sieťach so zámerom prínosu energetických úspor. K tejto téme už existujú štúdie, avšak väčšina z nich bola príliš špecifická k aplikácii alebo bola demonštrovaná v malom rozsahu. Pre dodatočné preskúmanie možností sme preto skrz netriviálne modifikácie open-source frameworku TensorFlow vytvorili platformu umožňujúcu simulovať používanie approximovaných obvodov na populárnych a robustných neurónových sieťach ako Inception alebo MobileNet. Bodom záujmu bolo nahradenie väčšiny výpočtovo náročných častí konvolučných neurónových si

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15

Rodrigues, Dirceu Zeferino. "Redes neurais, identidade de modelos e resposta da cebola à adubação nitrogenada." Universidade Federal de Viçosa, 2013. http://locus.ufv.br/handle/123456789/4064.

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Made available in DSpace on 2015-03-26T13:32:18Z (GMT). No. of bitstreams: 1 texto completo.pdf: 915073 bytes, checksum: b935760049a0fd3e2afd0852f0a37275 (MD5) Previous issue date: 2013-03-21<br>The study of the productivity curves compared with the amount of nitrogen absorbed by the onion crop is fundamentally important for the elaboration of a more efficient fertilization plan in technical terms as well as in economic terms. Many statistical techniques have been proposed, tested, and improved in order to help boost research in this direction. The justification for this research is the need

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16

Uppala, Roshni. "Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

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Andrade, Gustavo Araújo de. "PROGRAMAÇÃO DINÂMICA HEURÍSTICA DUAL E REDES DE FUNÇÕES DE BASE RADIAL PARA SOLUÇÃO DA EQUAÇÃO DE HAMILTON-JACOBI-BELLMAN EM PROBLEMAS DE CONTROLE ÓTIMO." Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/517.

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Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Dissertacao Gustavo Araujo.pdf: 2606649 bytes, checksum: efb1a5ded768b058f25d23ee8967bd38 (MD5) Previous issue date: 2014-04-28<br>In this work the main objective is to present the development of learning algorithms for online application for the solution of algebraic Hamilton-Jacobi-Bellman equation. The concepts covered are focused on developing the methodology for control systems, through techniques that aims to design online adaptive controllers to reject noise sensors, parametric variations and modeling error

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18

Chiu, Jih-Sheng, and 邱日聖. "Improving Asymmetric Approximate Search through Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/twwteg.

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碩士<br>國立嘉義大學<br>資訊工程學系研究所<br>106<br>Due to advance in information technology, we have to deal with growing digital data. The traditional linear search becomes impractical because of the large amount of data, so many researchers turn to develop approximate search methods. Before Approximate search, we have to do the clustering on data. In the search process, we compute the Euclidean distance between query and each cluster center, and then pick enough candidates according to their distances. However, the distance-based approach is not always the best way to pick candidates. In this study, we pro

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19

"Approximate Neural Networks for Speech Applications in Resource-Constrained Environments." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.39402.

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abstract: Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance, they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost of keyword detection and speech recognition networks (or DNNs) are presented. The first technique is based on representing all weights and biases by a small number of bits and mapping all nod

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20

(9178400), Sanchari Sen. "Efficient and Robust Deep Learning through Approximate Computing." Thesis, 2020.

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<p>Deep Neural Networks (DNNs) have greatly advanced the state-of-the-art in a wide range of machine learning tasks involving image, video, speech and text analytics, and are deployed in numerous widely-used products and services. Improvements in the capabilities of hardware platforms such as Graphics Processing Units (GPUs) and specialized accelerators have been instrumental in enabling these advances as they have allowed more complex and accurate networks to be trained and deployed. However, the enormous computational and memory demands of DNNs continue to increase with growing data size and

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Abdella, Mussa Ismael. "The use of genetic algorithms and neural networks to approximate missing data in database." Thesis, 2006. http://hdl.handle.net/10539/105.

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Missing data creates various problems in analysing and processing of data in databases. Due to this reason missing data has been an area of research in various disciplines for a quite long time. This report intro- duces a new method aimed at approximating missing data in a database using a combination of genetic algorithms and neural networks. The proposed method uses genetic algorithm to minimise an error function derived from an auto-associative neural network. The error function is expressed as the square of the di®erence between the actual observa- tions and p

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Pereira, Silvério Matos. "Anomaly detection in mobile networks." Master's thesis, 2021. http://hdl.handle.net/10773/31374.

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Big data has become an increasingly important topic in recent years, with new sources of data comes the need to be aware of the trade-off it requires, necessitating great care in both choice and implementation of algorithms, as well as how to adapt existing algorithms to handle this new setting. At the same time, the interpretability and understanding of a small to medium number of features is still key in many areas where understanding the data is paramount. In this thesis we show how we can tackle both these issues with the aid of self-organizing algorithms. Two objectives were achie

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(6634835), Syed Sarwar. "Exploration of Energy Efficient Hardware and Algorithms for Deep Learning." Thesis, 2019.

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<div>Deep Neural Networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, wearable) devices. Despite their success, they suffer from high energy requirements both in inference and training. In recent years, the inherent error resiliency of DNNs has been exploited by introducing approximations at either the algorithmic or the hardware levels (individually) to obtain energy savings while incurring tolerable accuracy degradation. We perform a comprehensive analysis to

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Chapados, Nicolas. "Sequential Machine learning Approaches for Portfolio Management." Thèse, 2009. http://hdl.handle.net/1866/3578.

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Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de fa

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