Дисертації з теми "Binary neural networks (BNN)"
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Simons, Taylor Scott. "High-Speed Image Classification for Resource-Limited Systems Using Binary Values." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/9097.
Повний текст джерелаBraga, AntoÌ‚nio de PaÌdua. "Design models for recursive binary neural networks." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336442.
Повний текст джерелаRedkar, Shrutika. "Deep Learning Binary Neural Network on an FPGA." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/407.
Повний текст джерелаEzzadeen, Mona. "Conception d'un circuit dédié au calcul dans la mémoire à base de technologie 3D innovante." Electronic Thesis or Diss., Aix-Marseille, 2022. http://theses.univ-amu.fr.lama.univ-amu.fr/221212_EZZADEEN_955e754k888gvxorp699jljcho_TH.pdf.
Повний текст джерелаWith the advent of edge devices and artificial intelligence, the data deluge is a reality, making energy-efficient computing systems a must-have. Unfortunately, classical von Neumann architectures suffer from the high cost of data transfers between memories and processing units. At the same time, CMOS scaling seems more and more challenging and costly to afford, limiting the chips' performance due to power consumption issues.In this context, bringing the computation directly inside or near memories (I/NMC) seems an appealing solution. However, data-centric applications require an important amount of non-volatile storage, and modern Flash memories suffer from scaling issues and are not very suited for I/NMC. On the other hand, emerging memory technologies such as ReRAM present very appealing memory performances, good scalability, and interesting I/NMC features. However, they suffer from variability issues and from a degraded density integration if an access transistor per bitcell (1T1R) is used to limit the sneak-path currents. This thesis work aims to overcome these two challenges. First, the variability impact on read and I/NMC operations is assessed and new robust and low-overhead ReRAM-based boolean operations are proposed. In the context of neural networks, new ReRAM-based neuromorphic accelerators are developed and characterized, with an emphasis on good robustness against variability, good parallelism, and high energy efficiency. Second, to resolve the density integration issues, an ultra-dense 3D 1T1R ReRAM-based Cube and its architecture are proposed, which can be used as a 3D NOR memory as well as a low overhead and energy-efficient I/NMC accelerator
Kennedy, John V. "The design of a scalable and application independent platform for binary neural networks." Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323503.
Повний текст джерелаLi, Guo. "Neural network for optimization of binary computer-generated hologram with printing model /." Online version of thesis, 1995. http://hdl.handle.net/1850/12234.
Повний текст джерелаMedvedieva, S. O., I. V. Bogach, V. A. Kovenko, С. О. Медведєва, І. В. Богач, and В. А. Ковенко. "Neural networks in Machine learning." Thesis, ВНТУ, 2019. http://ir.lib.vntu.edu.ua//handle/123456789/24788.
Повний текст джерелаThe paper covers the basic principles of Neural Networks’ work. Special attention is paid to Frank Rosenblatt’s model of the network called “perceptron”. In addition, the article touches upon the main programming languages used to write software for Neural Networks.
Wilson, Brittany Michelle. "Evaluating and Improving the SEU Reliability of Artificial Neural Networks Implemented in SRAM-Based FPGAs with TMR." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8619.
Повний текст джерелаMealey, Thomas C. "Binary Recurrent Unit: Using FPGA Hardware to Accelerate Inference in Long Short-Term Memory Neural Networks." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1524402925375566.
Повний текст джерелаStrandberg, Rickard, and Johan Låås. "A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252556.
Повний текст джерелаDet primära syftet med denna uppsats är att förutsäga huruvida en kund kommer köpa en specifik produkt eller ej. Den historiska datan tillhandahålls av den Nordiska internet-baserade IT-försäljaren Dustin. Det sekundära syftet med uppsatsen är att evaluera hur väl ett djupt neuralt nätverk presterar jämfört med Lasso regulariserad logistisk regression och gradient boostade träd (GXBoost). Denna uppsats fann att XGBoost presterade bättre än de två andra metoderna i såväl träffsäkerhet, som i hastighet.
Holesovsky, Ondrej. "Compact ConvNets with Ternary Weights and Binary Activations." Thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216389.
Повний текст джерелаKompakta arkitekturer, ternära vikter och binära aktiveringar är två metoder som är lämpliga för att göra neurala nätverk effektivare. Vi introducerar a) en dithering binär aktivering som förbättrar noggrannheten av ternärviktsnätverk med binära aktiveringar genom randomisering av kvantiseringsfel, och b) en metod för genomförande ternärviktsnätverk med binära aktiveringar med användning av binära operationer. Trots dessa nya metoder, att träna en kompakt SqueezeNet-arkitektur med ternära vikter och fullprecisionaktiveringar på ImageNet försämrar klassificeringsnoggrannheten betydligt mer än om man tränar en mindre kompakt arkitektur på samma sätt. Därför kan ternära vikter i deras nuvarande form inte kallas bästa sättet att minska nätverksstorleken. Emellertid, effekten av weight decay på träning av ternärviktsnätverk bör undersökas mer för att få större säkerhet i detta resultat.
Bergtold, Jason Scott. "Advances in Applied Econometrics: Binary Discrete Choice Models, Artificial Neural Networks, and Asymmetries in the FAST Multistage Demand System." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27266.
Повний текст джерелаPh. D.
Sibanda, Wilbert. "Comparative study of neural networks and design of experiments to the classification of HIV status / Wilbert Sibanda." Thesis, North West University, 2013. http://hdl.handle.net/10394/13179.
Повний текст джерелаAl-Shammaa, Mohammed. "Granular computing approach for intelligent classifier design." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13686.
Повний текст джерелаKindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.
Повний текст джерелаDet vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.
Nguyen, Thanh Le Vi. "Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2359.
Повний текст джерелаGardner, Angelica. "Stronger Together? An Ensemble of CNNs for Deepfakes Detection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97643.
Повний текст джерелаLe, Thu Anh. "An Exploration of the Word2vec Algorithm: Creating a Vector Representation of a Language Vocabulary that Encodes Meaning and Usage Patterns in the Vector Space Structure." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849728/.
Повний текст джерелаЛевчук, Святослав Богданович. "Інтелектуальна система мерчандайзингу. Детекція та розпізнавання асортименту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23987.
Повний текст джерелаMaster thesis explanatory note: 126 p., 47 fig., 30 tab., 2 appendices, 31 sources. The object of research – intelligent merchandising system. The subject of research – classification methods of goods on shelves in stores. The purpose of the work is to develop an intelligent merchandising system that will reduce the use of human resources and maximize the process of merchandising through automatic monitoring of the availability of goods on shelves and to develop of goods classification system as a part of a merchandising system for the analysis of goods on the shelf in relation to the store planograms. In the work, modern merchandising systems and their shortcomings are considered and analyzed, as well as existing classification methods are considered. Goods classification method with specially developed convolutional neural network, which is constructed on the basis of methods using convolutional neural networks, with nonlinear classifiers and an adaptive optimization method is proposed. Intelligent merchandising system and assortment classification system are implemented using Python programming language with MySql DB. The results of this work are recommended for monitoring the compliance with the planogram and availiability of the goods on shelves in stores.
Singh, Gurpreet. "Statistical Modeling of Dynamic Risk in Security Systems." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273599.
Повний текст джерелаBig data har använts regelbundet inom ekonomi för att bygga prognosmodeller, det är dock ett relativt nytt koncept inom säkerhetsbranschen. Denna studie förutsäger vilka larmkoder som kommer att låta under de kommande 7 dagarna på plats $L$ genom att observera de senaste 7 dagarna. Logistisk regression och neurala nätverk används för att lösa detta problem. Eftersom att problemet är av en multi-label natur tillämpas logistisk regression i kombination med binary relevance och classifier chains. Modellerna tränas på data som har annoterats med två separata metoder. Den första metoden annoterar datan genom att endast observera plats $L$ och den andra metoden betraktar $L$ och $L$:s omgivning. Eftersom problemet är multi-labeled kommer annoteringen sannolikt att vara obalanserad och därför används resamplings metoden, SMOTE, och random over-sampling för att öka frekvensen av minority labels. Recall, precision och F1-score mättes för att utvärdera modellerna. Resultaten visar att den andra annoterings metoden presterade bättre för alla modeller och att classifier chains och binary relevance presterade likartat. Binary relevance och classifier chains modellerna som tränades på datan som använts sig av resamplings metoden SMOTE gav ett högre macro average F1-score, dock sjönk prestationen för neurala nätverk. Resamplings metoden SMOTE presterade även bättre än random over-sampling. Neurala nätverksmodellen överträffade de andra två modellerna på alla metoder och uppnådde högsta F1-score.
Hedar, Sara. "Applying Machine Learning Methods to Predict the Outcome of Shots in Football." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414774.
Повний текст джерелаTeixeira, Alex Fernandes Rocha. "Identificação de uma coluna de destilação de metanol-água através de modelos paramétricos e redes neurais artificiais." Universidade Federal de Alagoas, 2011. http://www.repositorio.ufal.br/handle/riufal/1195.
Повний текст джерелаFoi realizado neste trabalho identificação caixa preta do processo de destilação Metanol-Água nas configurações malha aberta e malha fechada, utilizando como sinais de perturbação a função degrau e o Sinal Binário Pseudo-Aleatório (PRBS) para excitar a planta. Os modelos matemáticos candidatos a identificação foram as Redes Neurais Artificiais (RNA), e os modelos paramétricos discretos lineares autorregressivo com entradas externas (ARX do inglês AutoRegressive with eXogenous Inputs), autorregressivo com média móvel e entradas exógenas (ARMAX do inglês AutoRegressive Moving Average with eXogenous Inputs), modelo do tipo erro na saída (OE do inglês Output Error) e a estrutura Box-Jenkins (BJ). Com a disposição dos modelos, foram comparados quais dos modelos matemáticos candidatos à identificação melhor representa o processo coluna de destilação metanol-água. Comparou-se qual configuração do processo no ensaio de identificação para geração de dados apresenta mais vantagens, se em malha aberta ou em malha fechada, nas condições e metodologias utilizadas. Constatou-se a funcionalidade do sinal binário pseudo-aleatório como uma boa opção de excitação na identificação em malha aberta e fechada para sistemas dinâmicos.
Alamgir, Nyma. "Computer vision based smoke and fire detection for outdoor environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/201654/1/Nyma_Alamgir_Thesis.pdf.
Повний текст джерелаSantos, Cinara de Jesus. "Avaliação do uso de classificadores para verificação de atendimento a critérios de seleção em programas sociais." Universidade Federal de Juiz de Fora (UFJF), 2017. https://repositorio.ufjf.br/jspui/handle/ufjf/5582.
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Classificadores são separadores de grupos que mediante determinadas características organiza os dados agrupando elementos que apresentem traços semelhantes, o que permite reconhecimento de padrões e identificação de elementos que não se encaixam. Esse procedimento de classificação e separação pode ser observado em processos do cotidiano como exames (clínicos ou por imagem), separadores automáticos de grãos na agroindústria, identificador de probabilidades, reconhecedores de caracteres, identificação biométrica - digital, íris, face, etc. O estudo aqui proposto utiliza uma base de dados do Ministério do Desenvolvimento Social e Combate a Fome (MDS), contendo informações sobre beneficiários do Programa Bolsa Família (PBF), onde contamos com registros descritores do ambiente domiciliar, grau de instrução dos moradores do domicílio assim como o uso de serviços de saúde pelos mesmos e informações de cunho financeiro (renda e gastos das famílias). O foco deste estudo não visa avaliar o PBF, mas o comportamento de classificadores aplicados sobre bases de caráter social, pois estas apresentam certas particularidades. Sobre as variáveis que descrevem uma família como beneficiária ou não do PBF, testamos três algoritmos classificadores - regressão logística, árvore binária de decisão e rede neural artificial em múltiplas camadas. O desempenho destes processos foi medido a partir de métricas decorrentes da chamada matriz de confusão. Como os erros e acertos de uma classe n˜ao s˜ao os complementares da outra classe é de suma importância que ambas sejam corretamente identificadas. Um desempenho satisfatório para ambas as classes em um mesmo cenário não foi alçado - a identificação do grupo minoritário apresentou baixa eficiência mesmo com reamostragem seguida de reaplicação dos três processos classificatórios escolhidos, o que aponta para a necessidade de novos experimentos.
Classifiers are group separators that, by means of certain characteristics, organize the data by grouping elements that present similar traits, which allows pattern recognition and the identification of elements that do not fit. Classification procedures can be used in everyday processes such as clinical or imaging exams, automatic grain separators in agribusiness, probability identifiers, character recognition, biometric identification by thumbprints, iris, face, etc. This study uses a database of the Ministry of Social Development and Fight against Hunger (MDS), containing information on beneficiaries of the Bolsa Fam´ılia Program (PBF). The data describe the home environment, the level of education of the residents of the household, their use of public health services, and some financial information (income and expenses of families). The focus of this study is not to evaluate the PBF, but to analyze the performance of the classifiers when applied to bases of social character, since these have certain peculiarities. We have tested three classification algorithms - logistic regression, binary decision trees and artificial neural networks. The performance of these algorithms was measured by metrics computed from the so-called confusion matrix. As the probabilities of right and wrong classifications of a class are not complementary, it is of the utmost importance that both are correctly identified. A good evaluation could not be archive for both classes in a same scenario was not raised - the identification of the minority group showed low efficiency even with resampling followed by reapplication of the three classificatory processes chosen, which points to the need for new experiments.
Nguyen, Minh Ha Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Cooperative coevolutionary mixture of experts : a neuro ensemble approach for automatic decomposition of classification problems." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2006. http://handle.unsw.edu.au/1959.4/38752.
Повний текст джерелаNarayanan, Arun. "Computational auditory scene analysis and robust automatic speech recognition." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401460288.
Повний текст джерелаBeneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.
Повний текст джерелаFaula, Yannick. "Extraction de caractéristiques sur des images acquises en contexte mobile : Application à la reconnaissance de défauts sur ouvrages d’art." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI077.
Повний текст джерелаThe french railway network has a huge infrastructure which is composed of many civil engineering structures. These suffer from degradation of time and traffic and they are subject to a periodic monitoring in order to detect appearance of defects. At the moment, this inspection is mainly done visually by monitoring operators. Several companies test new vectors of photo acquisition like the drone, designed for civil engineering monitoring. In this thesis, the main goal is to develop a system able to detect, localize and save potential defects of the infrastructure. A huge issue is to detect sub-pixel defects like cracks in real time for improving the acquisition. For this task, a local analysis by thresholding is designed for treating large images. This analysis can extract some points of interest (FLASH points: Fast Local Analysis by threSHolding) where a straight line can sneak in. The smart spatial relationship of these points allows to detect and localise fine cracks. The results of the crack detection on concrete degraded surfaces coming from images of infrastructure show better performances in time and robustness than the state-of-art algorithms. Before the detection step, we have to ensure the acquired images have a sufficient quality to make the process. A bad focus or a movement blur are prohibited. We developed a method reusing the preceding computations to assess the quality in real time by extracting Local Binary Pattern (LBP) values. Then, in order to make an acquisition for photogrammetric reconstruction, images have to get a sufficient overlapping. Our algorithm, reusing points of interest of the detection, can make a simple matching between two images without using algorithms as type RANSAC. Our method has invariance in rotation, translation and scale range. After the acquisition, with images with optimal quality, it is possible to exploit methods more expensive in time like convolution neural networks. These are not able to detect cracks in real time but can detect other kinds of damages. However, the lack of data requires the constitution of our database. With approaches of independent classification (classifier SVM one-class), we developed a dynamic system able to evolve in time, detect and then classify the different kinds of damages. No system like ours appears in the literature for the defect detection on civil engineering structure. The implemented works on feature extraction on images for damage detection will be used in other applications as smart vehicle navigation or word spotting
Murach, Thomas. "Monoscopic Analysis of H.E.S.S. Phase II Data on PSR B1259–63/LS 2883." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18484.
Повний текст джерелаCherenkov telescopes can detect the faint Cherenkov light emitted by air showers that were initiated by cosmic particles with energies between approximately 100 GeV and 100 TeV in the Earth's atmosphere. Aiming for the detection of Cherenkov light emitted by gamma ray-initiated air showers, the vast majority of all detected showers are initiated by charged cosmic rays. In 2012 the H.E.S.S. observatory, until then comprising four telescopes with 100 m² mirrors each, was extended by adding a much larger fifth telescope with a very large mirror area of 600 m². Due to the large mirror area, this telescope has the lowest energy threshold of all telescopes of this kind. In this dissertation, a fast algorithm called MonoReco is presented that can reconstruct fundamental properties of the primary gamma rays like their direction or their energy. Furthermore, this algorithm can distinguish between air showers initiated either by gamma rays or by charged cosmic rays. Those tasks are accomplished with the help of artificial neural networks, which analyse moments of the intensity distributions in the camera of the new telescope exclusively. The energy threshold is 59 GeV and angular resolutions of 0.1°-0.3° are achieved. The energy reconstruction bias is at the level of a few percent, the energy resolution is at the level of 20-30%. Data taken around the 2014 periastron passage of the gamma-ray binary PSR B1259-63/LS 2883 were analysed with, among others, the MonoReco algorithm. This binary system comprises a neutron star in a 3.4 year orbit around a massive star with a circumstellar disk consisting of gas and plasma. For the first time the gamma-ray spectrum of this system could be measured by H.E.S.S. down to below 200 GeV. Furthermore, a local flux minimum could be measured during unprecedented measurements at the time of periastron. High fluxes were measured both before the first and after the second transit of the neutron star through the disk. In the second case measurements could be performed for the first time contemporaneously with the Fermi-LAT experiment, which has repeatedly detected very high fluxes at this part of the orbit. A good agreement between measured fluxes and predictions of a leptonic model is found.
"Applications of neural networks in the binary classification problem." 1997. http://library.cuhk.edu.hk/record=b5889310.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 1997.
Includes bibliographical references (leaves 125-127).
Chapter 1 --- Introduction --- p.10
Chapter 1.1 --- Overview --- p.10
Chapter 1.2 --- Classification Approaches --- p.11
Chapter 1.3 --- The Use of Neural Network --- p.12
Chapter 1.4 --- Motivations --- p.14
Chapter 1.5 --- Organization of Thesis --- p.16
Chapter 2 --- Related Work --- p.19
Chapter 2.1 --- Overview --- p.19
Chapter 2.2 --- Neural Network --- p.20
Chapter 2.2.1 --- Backpropagation Feedforward Neural Network --- p.20
Chapter 2.2.2 --- Training of a Backpropagation Feedforward Neural Network --- p.22
Chapter 2.2.3 --- Single Hidden-layer Model --- p.27
Chapter 2.2.4 --- Data Preprocessing --- p.27
Chapter 2.3 --- Fuzzy Sets --- p.29
Chapter 2.3.1 --- Fuzzy Linear Regression Analysis --- p.29
Chapter 2.4 --- Network Architecture Altering Algorithms --- p.31
Chapter 2.4.1 --- Pruning Algorithms --- p.32
Chapter 2.4.2 --- Constructive/Growing Algorithms --- p.35
Chapter 2.5 --- Summary --- p.38
Chapter 3 --- Hybrid Classification Systems --- p.39
Chapter 3.1 --- Overview --- p.39
Chapter 3.2 --- Literature Review --- p.41
Chapter 3.2.1 --- Fuzzy Linear Regression(FLR) with Fuzzy Interval Analysis --- p.41
Chapter 3.3 --- Data Sample and Methodology --- p.44
Chapter 3.4 --- Hybrid Model --- p.46
Chapter 3.4.1 --- Construction of Model --- p.46
Chapter 3.5 --- Experimental Results --- p.50
Chapter 3.5.1 --- Experimental Results on Breast Cancer Database --- p.50
Chapter 3.5.2 --- Experimental Results on Synthetic Data --- p.53
Chapter 3.6 --- Conclusion --- p.55
Chapter 4 --- Searching for Suitable Network Size Automatically --- p.59
Chapter 4.1 --- Overview --- p.59
Chapter 4.2 --- Literature Review --- p.61
Chapter 4.2.1 --- Pruning Algorithm --- p.61
Chapter 4.2.2 --- Constructive Algorithms (Growing) --- p.66
Chapter 4.2.3 --- Integration of methods --- p.67
Chapter 4.3 --- Methodology and Approaches --- p.68
Chapter 4.3.1 --- Growing --- p.68
Chapter 4.3.2 --- Combinations of Growing and Pruning --- p.69
Chapter 4.4 --- Experimental Results --- p.75
Chapter 4.4.1 --- Breast-Cancer Cytology Database --- p.76
Chapter 4.4.2 --- Tic-Tac-Toe Database --- p.82
Chapter 4.5 --- Conclusion --- p.89
Chapter 5 --- Conclusion --- p.91
Chapter 5.1 --- Recall of Thesis Objectives --- p.91
Chapter 5.2 --- Summary of Achievements --- p.92
Chapter 5.2.1 --- Data Preprocessing --- p.92
Chapter 5.2.2 --- Network Size --- p.93
Chapter 5.3 --- Future Works --- p.94
Chapter A --- Experimental Results of Ch3 --- p.95
Chapter B --- Experimental Results of Ch4 --- p.112
Bibliography --- p.125
TANG, CHI-HUAN, and 唐其煥. "Low-cost Design and Implementation for Binary Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c5aa76.
Повний текст джерела國立高雄應用科技大學
電子工程系
106
In recent years, deep learning has been one of the most popular subject in academia and widely used in many fields such as computer vision, image classification, motion recognition, voice recognition, and big-data analysis tasks. Although the larger neural network architecture can improve accuracy obviously, the cost of memory usage, power consumption and time consumption also increase. How to use memory and speed effectively to achieve a certain accuracy has been the most popular subject in recent years. In the first part of this thesis, we will introduce the development of convolution neural network in recent years, and then we will introduce and explore the diversification of binary neural network. Finally, we will focus on Deep Residual Network and propose our method to improved XNOR-Net. By adjusting Deep Residual Network basic structure, increasing the possible of input layer and replacing more simply bit counter than multiplier, we can simplify large network architecture and increase accuracy than previous network greatly. The experimental results demonstrate that our design achieves the same performances in memory usage as XNOR-Net. Moreover, it can dramatically increase accuracy in Cifar10/Cifar100 datasets, and achieve the good accuracy result than other binary neural network paper.
"Approaches to the implementation of binary relation inference network." Chinese University of Hong Kong, 1994. http://library.cuhk.edu.hk/record=b5888221.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 1994.
Includes bibliographical references (leaves 96-98).
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The Availability of Parallel Processing Machines --- p.2
Chapter 1.1.1 --- Neural Networks --- p.5
Chapter 1.2 --- Parallel Processing in the Continuous-Time Domain --- p.6
Chapter 1.3 --- Binary Relation Inference Network --- p.10
Chapter 2 --- Binary Relation Inference Network --- p.12
Chapter 2.1 --- Binary Relation Inference Network --- p.12
Chapter 2.1.1 --- Network Structure --- p.14
Chapter 2.2 --- Shortest Path Problem --- p.17
Chapter 2.2.1 --- Problem Statement --- p.17
Chapter 2.2.2 --- A Binary Relation Inference Network Solution --- p.18
Chapter 3 --- A Binary Relation Inference Network Prototype --- p.21
Chapter 3.1 --- The Prototype --- p.22
Chapter 3.1.1 --- The Network --- p.22
Chapter 3.1.2 --- Computational Element --- p.22
Chapter 3.1.3 --- Network Response Time --- p.27
Chapter 3.2 --- Improving Response --- p.29
Chapter 3.2.1 --- Removing Feedback --- p.29
Chapter 3.2.2 --- Selecting Minimum with Diodes --- p.30
Chapter 3.3 --- Speeding Up the Network Response --- p.33
Chapter 3.4 --- Conclusion --- p.35
Chapter 4 --- VLSI Building Blocks --- p.36
Chapter 4.1 --- The Site --- p.37
Chapter 4.2 --- The Unit --- p.40
Chapter 4.2.1 --- A Minimum Finding Circuit --- p.40
Chapter 4.2.2 --- A Tri-state Comparator --- p.44
Chapter 4.3 --- The Computational Element --- p.45
Chapter 4.3.1 --- Network Performances --- p.46
Chapter 4.4 --- Discussion --- p.47
Chapter 5 --- A VLSI Chip --- p.48
Chapter 5.1 --- Spatial Configuration --- p.49
Chapter 5.2 --- Layout --- p.50
Chapter 5.2.1 --- Computational Elements --- p.50
Chapter 5.2.2 --- The Network --- p.52
Chapter 5.2.3 --- I/O Requirements --- p.53
Chapter 5.2.4 --- Optional Modules --- p.53
Chapter 5.3 --- A Scalable Design --- p.54
Chapter 6 --- The Inverse Shortest Paths Problem --- p.57
Chapter 6.1 --- Problem Statement --- p.59
Chapter 6.2 --- The Embedded Approach --- p.63
Chapter 6.2.1 --- The Formulation --- p.63
Chapter 6.2.2 --- The Algorithm --- p.65
Chapter 6.3 --- Implementation Results --- p.66
Chapter 6.4 --- Other Implementations --- p.67
Chapter 6.4.1 --- Sequential Machine --- p.67
Chapter 6.4.2 --- Parallel Machine --- p.68
Chapter 6.5 --- Discussion --- p.68
Chapter 7 --- Closed Semiring Optimization Circuits --- p.71
Chapter 7.1 --- Transitive Closure Problem --- p.72
Chapter 7.1.1 --- Problem Statement --- p.72
Chapter 7.1.2 --- Inference Network Solutions --- p.73
Chapter 7.2 --- Closed Semirings --- p.76
Chapter 7.3 --- Closed Semirings and the Binary Relation Inference Network --- p.79
Chapter 7.3.1 --- Minimum Spanning Tree --- p.80
Chapter 7.3.2 --- VLSI Implementation --- p.84
Chapter 7.4 --- Conclusion --- p.86
Chapter 8 --- Conclusions --- p.87
Chapter 8.1 --- Summary of Achievements --- p.87
Chapter 8.2 --- Future Work --- p.89
Chapter 8.2.1 --- VLSI Fabrication --- p.89
Chapter 8.2.2 --- Network Robustness --- p.90
Chapter 8.2.3 --- Inference Network Applications --- p.91
Chapter 8.2.4 --- Architecture for the Bellman-Ford Algorithm --- p.91
Bibliography --- p.92
Appendices --- p.99
Chapter A --- Detailed Schematic --- p.99
Chapter A.1 --- Schematic of the Inference Network Structures --- p.99
Chapter A.1.1 --- Unit with Self-Feedback --- p.99
Chapter A.1.2 --- Unit with Self-Feedback Removed --- p.100
Chapter A.1.3 --- Unit with a Compact Minimizer --- p.100
Chapter A.1.4 --- Network Modules --- p.100
Chapter A.2 --- Inference Network Interface Circuits --- p.100
Chapter B --- Circuit Simulation and Layout Tools --- p.107
Chapter B.1 --- Circuit Simulation --- p.107
Chapter B.2 --- VLSI Circuit Design --- p.110
Chapter B.3 --- VLSI Circuit Layout --- p.111
Chapter C --- The Conjugate-Gradient Descent Algorithm --- p.113
Chapter D --- Shortest Path Problem on MasPar --- p.115
Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ernet.in/2005/3581.
Повний текст джерелаLian, Chi-Li, and 連崔立. "Using Probabilistic Neural Networks and Binary Sequence Algorithm to Build Financial Prediction Models - A Case of the Electronic Industry in Taiwan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/3t54vc.
Повний текст джерела國立臺北科技大學
工業工程與管理研究所
96
This research attempts to use probabilistic neural networks(PNN) and binary sequence algorithm(BSA) to build financial prediction models, regard listed company as the research object, take three annual financial materials of company. The main purpose to build this financial prediction models, lie in finding the potential financial crisis inside enterprises ahead of time, offer investors and electronic industry one to consult alert news by this. This research is divided into two stages and built the model, the first stage is to use two kinds of data type and four kinds of period to build financial classification model, elect the best model to produce the classifying value, The second stage is to rise from these classifying value prediction pattern through BSA, predicting via these prediction patterns. Looked by the real example result, with appropriate prediction pattern, can offer better prediction result.
Lin, Zhouhan. "Deep neural networks for natural language processing and its acceleration." Thèse, 2019. http://hdl.handle.net/1866/23438.
Повний текст джерелаThis thesis by article consists of four articles which contribute to the field of deep learning, specifically in the acceleration of training through low-precision networks, and the application of deep neural networks on natural language processing. In the first article, we investigate a neural network training scheme that eliminates most of the floating-point multiplications. This approach consists of binarizing or ternarizing the weights in the forward propagation and quantizing the hidden states in the backward propagation, which converts multiplications to sign changes and binary shifts. Experimental results on datasets from small to medium size show that this approach result in even better performance than standard stochastic gradient descent training, paving the way to fast, hardware-friendly training of neural networks. In the second article, we proposed a structured self-attentive sentence embedding that extracts interpretable sentence representations in matrix form. We demonstrate improvements on 3 different tasks: author profiling, sentiment classification and textual entailment. Experimental results show that our model yields a significant performance gain compared to other sentence embedding methods in all of the 3 tasks. In the third article, we propose a hierarchical model with dynamical computation graph for sequential data that learns to construct a tree while reading the sequence. The model learns to create adaptive skip-connections that ease the learning of long-term dependencies through constructing recurrent cells in a recursive manner. The training of the network can either be supervised training by giving golden tree structures, or through reinforcement learning. We provide preliminary experiments in 3 different tasks: a novel Math Expression Evaluation (MEE) task, a well-known propositional logic task, and language modelling tasks. Experimental results show the potential of the proposed approach. In the fourth article, we propose a novel constituency parsing method with neural networks. The model predicts the parse tree structure by predicting a real valued scalar, named syntactic distance, for each split position in the input sentence. The order of the relative values of these syntactic distances then determine the parse tree structure by specifying the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Our proposed approach was demonstrated with competitive performance on Penn Treebank dataset, and the state-of-the-art performance on Chinese Treebank dataset.
Gurioli, Gianmarco. "Adaptive Regularisation Methods under Inexact Evaluations for Nonconvex Optimisation and Machine Learning Applications." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238314.
Повний текст джерелаCroon, Dennis Gerardus. "The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks." Master's thesis, 2020. http://hdl.handle.net/10362/103901.
Повний текст джерелаExtensive recent research has shown the importance of innovation in medical healthcare, with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast as possible and preferably in advance of the symptoms. An online database source managed to gather Pneumonia-specific image data, with not just the presence of the infection, but also the nature of it, divided in bacterial- and viral infection. The first achievement is extracting valuable information from the X-Ray image datasets. Using several ImageNet pre-trained CNNs, knowledge can be gained from images and transferred to numeric arrays. This, both binary and multi-class classification data, requires a sophisticated prediction algorithm that recognizes X-Ray image patterns. Multiple, recently performed experiments show promising results about the innovative Semantic Learning Machine (SLM) that is essentially a geometric semantic hill climber for feedforward Neural Networks. This SLM is based on a derivation of the Geometric Semantic Genetic Programming (GSGP) mutation operator for real-value semantics. To prove the outperformance of the binary and multi-class SLM in general, a selection of commonly used algorithms is necessary in this research. A comprehensive hyperparameter optimization is performed for commonly used algorithms for those kinds of real-life problems, such as: Random Forest, Support Vector Machine, KNearestNeighbors and Neural Networks. The results of the SLM are promising for the Pneumonia application but could be used for all types of predictions based on images in combination with the CNN feature extractions.
Uma extensa pesquisa recente mostrou a importância da inovação na assistência médica, com foco na pneumonia. É vital e salva-vidas prever os casos de pneumonia o mais rápido possível e, de preferência, antes dos sintomas. Uma fonte on-line conseguiu coletar dados de imagem específicos da pneumonia, identificando não apenas a presença da infecção, mas também seu tipo, bacteriana ou viral. A primeira conquista é extrair informações valiosas dos conjuntos de dados de imagem de raios-X. Usando várias CNNs pré-treinadas da ImageNet, é possível obter conhecimento das imagens e transferi-las para matrizes numéricas. Esses dados de classificação binários e multi-classe requerem um sofisticado algoritmo de predição que reconhece os padrões de imagem de raios-X. Vários experimentos realizados recentemente mostram resultados promissores sobre a inovadora Semantic Learning Machine (SLM), que é essencialmente um hill climber semântico geométrico para feedforward neural network. Esse SLM é baseado em uma derivação do operador de mutação da Geometric Semantic Genetic Programming (GSGP) para valor-reais semânticos. Para provar o desempenho superior do SLM binário e multi-classe em geral, é necessária uma seleção de algoritmos mais comuns na pesquisa. Uma otimização abrangente dos hiperparâmetros é realizada para algoritmos comumente utilizados para esses tipos de problemas na vida real, como Random Forest, Support Vector Machine,K-Nearest Neighbors and Neural Networks. Os resultados do SLM são promissores para o aplicativo pneumonia, mas podem ser usados para todos os tipos de previsões baseadas em imagens em combinação com as extrações de recursos da CNN.
Silva, André de Vasconcelos Santos. "Sparse distributed representations as word embeddings for language understanding." Master's thesis, 2018. http://hdl.handle.net/10071/18245.
Повний текст джерелаA designação word embeddings refere-se a representações vetoriais das palavras que capturam as similaridades semânticas e sintáticas entre estas. Palavras similares tendem a ser representadas por vetores próximos num espaço N dimensional considerando, por exemplo, a distância Euclidiana entre os pontos associados a estas representações vetoriais num espaço vetorial contínuo. Esta propriedade, torna as word embeddings importantes em várias tarefas de Processamento Natural da Língua, desde avaliações de analogia e similaridade entre palavras, às mais complexas tarefas de categorização, sumarização e tradução automática de texto. Tipicamente, as word embeddings são constituídas por vetores densos, de dimensionalidade reduzida. São obtidas a partir de aprendizagem não supervisionada, recorrendo a consideráveis quantidades de dados, através da otimização de uma função objetivo de uma rede neuronal. Este trabalho propõe uma metodologia para obter word embeddings constituídas por vetores binários esparsos, ou seja, representações vetoriais das palavras simultaneamente binárias (e.g. compostas apenas por zeros e uns), esparsas e com elevada dimensionalidade. A metodologia proposta tenta superar algumas desvantagens associadas às metodologias do estado da arte, nomeadamente o elevado volume de dados necessário para treinar os modelos, e simultaneamente apresentar resultados comparáveis em várias tarefas de Processamento Natural da Língua. Os resultados deste trabalho mostram que estas representações, obtidas a partir de uma quantidade limitada de dados de treino, obtêm performances consideráveis em tarefas de similaridade e categorização de palavras. Por outro lado, em tarefas de analogia de palavras apenas se obtém resultados consideráveis para a categoria gramatical dos substantivos. As word embeddings obtidas com a metodologia proposta, e comparando com o estado da arte, superaram a performance de oito word embeddings em tarefas de similaridade, e de duas word embeddings em tarefas de categorização de palavras.
Venables, Anne. "Ecological and biological modeling for natural resource management: applications to wetland classification and evaluation." Thesis, 2014. https://vuir.vu.edu.au/25869/.
Повний текст джерела