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Dissertations / Theses on the topic 'Artificial Neural Networks and Recurrent Neutral Networks'

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Published: 13 August 2024

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1

Kolen, John F. "Exploring the computational capabilities of recurrent neural networks /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487853913100192.

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2

Shao, Yuanlong. "Learning Sparse Recurrent Neural Networks in Language Modeling." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398942373.

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3

Gudjonsson, Ludvik. "Comparison of two methods for evolving recurrent artificial neural networks for." Thesis, University of Skövde, University of Skövde, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-155.

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<p>n this dissertation a comparison of two evolutionary methods for evolving ANNs for robot control is made. The methods compared are SANE with enforced sub-population and delta-coding, and marker-based encoding. In an attempt to speed up evolution, marker-based encoding is extended with delta-coding. The task selected for comparison is the hunter-prey task. This task requires the robot controller to posess some form of memory as the prey can move out of sensor range. Incremental evolution is used to evolve the complex behaviour that is required to successfully handle this task. The comparison

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4

Parfitt, Shan Helen. "Explorations in anaphora resolution in artificial neural networks : implications for nativism." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267247.

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5

NAPOLI, CHRISTIAN. "A-I: Artificial intelligence." Doctoral thesis, Università degli studi di Catania, 2016. http://hdl.handle.net/20.500.11769/490996.

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In this thesis we proposed new neural architectures and information theory approaches. By means of wavelet analysis, neural networks, and the results of our own creations, namely the wavelet recurrent neural networks and the radial basis probabilistic neural networks,we tried to better understand, model and cope with the human behavior itself. The first idea was to model the workers of a crowdsourcing project as nodes on a cloud-computing system, we also hope to have exceeded the limits of such a definition. We hope to have opened a door on new possibilities to model the behavior of socially i

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6

Kramer, Gregory Robert. "An analysis of neutral drift's effect on the evolution of a CTRNN locomotion controller with noisy fitness evaluation." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1182196651.

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7

Rallabandi, Pavan Kumar. "Processing hidden Markov models using recurrent neural networks for biological applications." Thesis, University of the Western Cape, 2013. http://hdl.handle.net/11394/4525.

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Philosophiae Doctor - PhD<br>In this thesis, we present a novel hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov Models (HMMs). Though sequence recognition problems could be potentially modelled through well trained HMMs, they could not provide a reasonable solution to the complicated recognition problems. In contrast, the ability of RNNs to recognize the complex sequence recognition problems is known to be exceptionally good. It should be noted that in the past, methods for applying HMMs into RNNs have be

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8

Salihoglu, Utku. "Toward a brain-like memory with recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210221.

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For the last twenty years, several assumptions have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural network should be coded in some way or another in one of the dynamical attractors of the brain, and retrieved by stimulating the network to trap its dynamics in the desired item’s basin of attraction. The second view shared by neural network researchers is to base

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9

Yang, Jidong. "Road crack condition performance modeling using recurrent Markov chains and artificial neural networks." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000567.

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10

Willmott, Devin. "Recurrent Neural Networks and Their Applications to RNA Secondary Structure Inference." UKnowledge, 2018. https://uknowledge.uky.edu/math_etds/58.

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Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for

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11

Napoli, Christian. "A-I: Artificial intelligence." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3974.

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In this thesis we proposed new neural architectures and information theory approaches. By means of wavelet analysis, neural networks, and the results of our own creations, namely the wavelet recurrent neural networks and the radial basis probabilistic neural networks,we tried to better understand, model and cope with the human behavior itself. The first idea was to model the workers of a crowdsourcing project as nodes on a cloud-computing system, we also hope to have exceeded the limits of such a definition. We hope to have opened a door on new possibilities to model the behavior of socially

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12

Vikström, Filip. "A recurrent neural network approach to quantification of risks surrounding the Swedish property market." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126192.

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As the real estate market plays a central role in a countries financial situation, as a life insurer, a bank and a property developer, Skandia wants a method for better assessing the risks connected to the real estate market. The goal of this paper is to increase the understanding of property market risk and its covariate risks and to conduct an analysis of how a fall in real estate prices could affect Skandia’s exposed assets.This paper explores a recurrent neural network model with the aim of quantifying identified risk factors using exogenous data. The recurrent neural network model is comp

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13

Condarcure, Thomas A. 1952. "A learning automaton approach to trajectory learning and control system design using dynamic recurrent neural networks." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/291987.

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This thesis presents a method for the training of dynamic, recurrent neural networks to generate continuous-time trajectories. In the past, most methods for this type of training were based on gradient descent methods and were deterministic. The method presented here is stochastic in nature. The problem of local minima is addressed by adding the enhancement of incremental learning to the learning automaton; i.e., small learning goals are used to train the neural network from its initialized state to its final parameters for the desired response. The method is applied to the learning of a bench

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14

Gattoni, Giacomo. "Improving the reliability of recurrent neural networks while dealing with bad data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In practical applications, machine learning and deep learning models can have difficulty in achieving generalization, especially when dealing with training samples that are either noisy or limited in quantity. Standard neural networks do not guarantee the monotonicity of the input features with respect to the output, therefore they lack interpretability and predictability when it is known a priori that the input-output relationship should be monotonic. This problem can be encountered in the CPG industry, where it is not possible to ensure that a deep learning model will learn the increasing

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15

Grose, Mitchell. "Forecasting Atmospheric Turbulence Conditions From Prior Environmental Parameters Using Artificial Neural Networks: An Ensemble Study." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619632748733788.

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16

Lindell, Adam. "Pulse Repetition Interval Time Series Modeling for Radar Waves using Long Short-Term Memory Artificial Recurrent Neural Networks." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-377865.

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This project is a performance study of Long Short-Term Memory artificial neural networks in the context of a specific time series prediction problem consisting of radar pulse trains. The network is tested both in terms of accuracy on a regular time series but also on an incomplete time series where values have been removed in order to test its robustness/resistance to small errors. The results indicate that the network can perform very well when no values are removed and can be trained relatively quickly using the parameters set in this project, although the robustness of the network seems to

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Svebrant, Henrik. "Latent variable neural click models for web search." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232311.

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User click modeling in web search is most commonly done through probabilistic graphical models. Due to the successful use of machine learning techniques in other fields of research, it is interesting to evaluate how machine learning can be applied to click modeling. In this thesis, modeling is done using recurrent neural networks trained on a distributed representation of the state of the art user browsing model (UBM). It is further evaluated how extending this representation with a set of latent variables that are easily derivable from click logs, can affect the model's prediction performance

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18

Chancan, Leon Marvin Aldo. "The role of motion-and-visual perception in robot place learning and navigation." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229769/8/Marvin%20Aldo_Chancan%20Leon_Thesis.pdf.

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This thesis was a step forward in developing new robot learning-based localisation and navigation systems using real world data and simulation environments. Three new methods were proposed to provide new insights on the role of joint motion-and-vision-based end-to-end robot learning in both place recognition and navigation tasks, within modern reinforcement learning and deep learning frameworks. Inspired by biological neural circuits underlying these complex tasks in insect and rat mammalian brains, these methods were shown to be orders of magnitude faster than classical techniques, while sett

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19

Canaday, Daniel M. "Modeling and Control of Dynamical Systems with Reservoir Computing." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157469471458874.

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20

Max, Lindblad. "The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223966.

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This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. Comparisons between the parsing methods have been made by first training long short-term memory (LSTM) recurrent neural networks (RNN) on each of the parsed datas

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21

Mohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a L

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Bahceci, Oktay. "Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210252.

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Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements an

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23

Forslund, John, and Jesper Fahlén. "Predicting customer purchase behavior within Telecom : How Artificial Intelligence can be collaborated into marketing efforts." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279575.

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This study aims to investigate the implementation of an AI model that predicts customer purchases, in the telecom industry. The thesis also outlines how such an AI model can assist decision-making in marketing strategies. It is concluded that designing the AI model by following a Recurrent Neural Network (RNN) architecture with a Long Short-Term Memory (LSTM) layer, allow for a successful implementation with satisfactory model performances. Stepwise instructions to construct such model is presented in the methodology section of the study. The RNN-LSTM model further serves as an assisting tool

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24

Howard, Shaun Michael. "Deep Learning for Sensor Fusion." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495751146601099.

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25

Kišš, Martin. "Rozpoznávání historických textů pomocí hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385912.

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The aim of this work is to create a tool for automatic transcription of historical documents. The work is mainly focused on the recognition of texts from the period of modern times written using font Fraktur. The problem is solved with a newly designed recurrent convolutional neural networks and a Spatial Transformer Network. Part of the solution is also an implemented generator of artificial historical texts. Using this generator, an artificial data set is created on which the convolutional neural network for line recognition is trained. This network is then tested on real historical lines of

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26

Křepský, Jan. "Rekurentní neuronové sítě v počítačovém vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237029.

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The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.

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27

Etienne, Caroline. "Apprentissage profond appliqué à la reconnaissance des émotions dans la voix." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS517.

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Mes travaux de thèse s'intéressent à l'utilisation de nouvelles technologies d'intelligence artificielle appliquées à la problématique de la classification automatique des séquences audios selon l'état émotionnel du client au cours d'une conversation avec un téléconseiller. En 2016, l'idée est de se démarquer des prétraitements de données et modèles d'apprentissage automatique existant au sein du laboratoire, et de proposer un modèle qui soit le plus performant possible sur la base de données audios IEMOCAP. Nous nous appuyons sur des travaux existants sur les modèles de réseaux de neurones pr

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Schäfer, Anton Maximilian. "Reinforcement Learning with Recurrent Neural Networks." Doctoral thesis, 2008. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2008112111.

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Controlling a high-dimensional dynamical system with continuous state and action spaces in a partially unknown environment like a gas turbine is a challenging problem. So far often hard coded rules based on experts´ knowledge and experience are used. Machine learning techniques, which comprise the field of reinforcement learning, are generally only applied to sub-problems. A reason for this is that most standard RL approaches still fail to produce satisfactory results in those complex environments. Besides, they are rarely data-efficient, a fact which is crucial for most real-world application

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Rodríguez, Sotelo José Manuel. "Speech synthesis using recurrent neural networks." Thèse, 2016. http://hdl.handle.net/1866/19111.

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30

Chung, Junyoung. "On Deep Multiscale Recurrent Neural Networks." Thèse, 2018. http://hdl.handle.net/1866/21588.

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31

Rossi, Alberto. "Siamese and Recurrent neural networks for Medical Image Processing." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238384.

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In recent years computer vision applications have been pervaded by deep convolutional neural networks (CNNs). These networks allow practitioners to achieve the state of the art performance at least for the segmentation and classification of images and in object localization, but in each of these cases the obtained results are directly correlated with the size of the training set, the quality of the annotations, the network depth and the power of modern GPUs. The same rules apply to medical image analysis, although, in this case, collecting tagged images is more difficult than ever, due to the

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32

Lin, Wen-chung, and 林文中. "Qualitative Modeling of Genetic Regulatory Networks via Recurrent Artificial Neural Network." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/76416405983136620066.

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碩士<br>長庚大學<br>資訊管理研究所<br>90<br>According to the statistical abstract from Department of Health, Taiwan, R.O.C., 2001, cancer is still in the fist place in the cause of the death. Because of this reason, the therapy of cancer is widely emphasized on. Clinically, we can not tell the specific difference between the normal cell and cancer cell. This is one of the barriers to develop the therapy of cancer. Owing to these, a proposed qualitative model in order to help gene-related-disease workers to understand and reason the effect of toxic chemicals and medicines that are capable of activating or i

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33

Krueger, David. "Designing Regularizers and Architectures for Recurrent Neural Networks." Thèse, 2016. http://hdl.handle.net/1866/14019.

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34

Peterson, Cole. "Generating rhyming poetry using LSTM recurrent neural networks." Thesis, 2019. http://hdl.handle.net/1828/10801.

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Current approaches to generating rhyming English poetry with a neural network involve constraining output to enforce the condition of rhyme. We investigate whether this approach is necessary, or if recurrent neural networks can learn rhyme patterns on their own. We compile a new dataset of amateur poetry which allows rhyme to be learned without external constraints because of the dataset’s size and high frequency of rhymes. We then evaluate models trained on the new dataset using a novel framework that automatically measures the system’s knowledge of poetic form and generalizability. We

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35

Anbil, Parthipan Sarath Chandar. "On challenges in training recurrent neural networks." Thèse, 2019. http://hdl.handle.net/1866/23435.

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Dans un problème de prédiction à multiples pas discrets, la prédiction à chaque instant peut dépendre de l’entrée à n’importe quel moment dans un passé lointain. Modéliser une telle dépendance à long terme est un des problèmes fondamentaux en apprentissage automatique. En théorie, les Réseaux de Neurones Récurrents (RNN) peuvent modéliser toute dépendance à long terme. En pratique, puisque la magnitude des gradients peut croître ou décroître exponentiellement avec la durée de la séquence, les RNNs ne peuvent modéliser que les dépendances à court terme. Cette thèse explore ce problème dans les

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36

Zhu, Yuqing. "Nonlinear system identification using a genetic algorithm and recurrent artificial neural networks." Thesis, 2006. http://spectrum.library.concordia.ca/9060/1/MR20771.pdf.

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In this study, the application of Recurrent Artificial Neural Network (RANN) in nonlinear system identification has been extensively explored. Three RANN-based identification models have been presented to describe the behavior of the nonlinear systems. The approximation accuracy of RANN-based models relies on two key factors: architecture and weights. Due to its inherent property of parallelism and evolutionary mechanism, a Genetic Algorithm (GA) becomes a promising technique to obtain good neural network architecture. A GA is developed to approach the optimal architecture of a RANN with multi

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Ghazi-Zahedi, Keyan Mahmoud. "Self-Regulating Neurons. A model for synaptic plasticity in artificial recurrent neural networks." Doctoral thesis, 2009. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2009020616.

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Robustness and adaptivity are important behavioural properties observed in biological systems, which are still widely absent in artificial intelligence applications. Such static or non-plastic artificial systems are limited to their very specific problem domain. This work introducesa general model for synaptic plasticity in embedded artificial recurrent neural networks, which is related to short-term plasticity by synaptic scaling in biological systems. The model is general in the sense that is does not require trigger mechanisms or artificial limitations and it operates on recurrent neural ne

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Boulanger-Lewandowski, Nicolas. "Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks." Thèse, 2014. http://hdl.handle.net/1866/11181.

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Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisatio

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Kanuparthi, Bhargav. "Towards better understanding and improving optimization in recurrent neural networks." Thesis, 2020. http://hdl.handle.net/1866/24319.

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Recurrent neural networks (RNN) are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because it prevents important gradient components from being back-propagated adequately over a large number of steps. The papers written in this work formalizes gradient propagation in parametric and semi-parametric RNNs to gain a better understanding towards the source of this problem. The first paper introduces a simple stochastic algorithm (h-detach) that is

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Mehri, Soroush. "Sequential modeling, generative recurrent neural networks, and their applications to audio." Thèse, 2016. http://hdl.handle.net/1866/18762.

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Agrawal, Harish. "Novel Neural Architectures based on Recurrent Connections and Symmetric Filters for Visual Processing." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6022.

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Artificial Neural Networks (ANN) have been very successful due to their ability to extract meaningful information without any need for pre-processing raw data. First artificial neural networks were created in essence to understand how the human brain works. The expectations were that we would get a deeper understanding of the brain functions and human cognition, which we cannot explain just by biological experiments or intuitions. The field of ANN has grown so much now that the ANNs are not only limited for the purpose which they emerged for but are also being exploited for their unmatched pat

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Ghazi-Zahedi, Keyan Mahmoud [Verfasser]. "Self-regulating neurons : a model for synaptic plasticity in artificial recurrent neural networks / Keyan Mahmoud Ghazi-Zahedi." 2009. http://d-nb.info/992767202/34.

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Sainath, Pravish. "Modeling functional brain activity of human working memory using deep recurrent neural networks." Thesis, 2020. http://hdl.handle.net/1866/25468.

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Dans les systèmes cognitifs, le rôle de la mémoire de travail est crucial pour le raisonnement visuel et la prise de décision. D’énormes progrès ont été réalisés dans la compréhension des mécanismes de la mémoire de travail humain/animal, ainsi que dans la formulation de différents cadres de réseaux de neurones artificiels à mémoire augmentée. L’objectif global de notre projet est de former des modèles de réseaux de neurones artificiels capables de consolider la mémoire sur une courte période de temps pour résoudre une tâche de mémoire et les relier à l’activité cérébrale des humains qui on

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Ting, Chien-Chung, and 丁建中. "Robust Stabilization Analysis and Estimator Design for Uncertain Neutral Recurrent Neural Networks with Interval Time-varying Discrete and Distributed Delays." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58694883170618759753.

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碩士<br>國立彰化師範大學<br>工業教育與技術學系<br>98<br>This thesis presents the complete study of stability analysis and state estimators design. The system is focused on neutral neural networks with both interval discrete and distributed time-varying delays, where the time-varying delays are in a given range. In a stability analysis problem, the purpose is to develop globally robust delay-dependent stability for neutral uncertain neural networks with both discrete and distributed delays. The activation functions are supposed to be bounded and globally Lipschitz continuous. By using a Lyapunov function approach

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Laurent, César. "Advances in parameterisation, optimisation and pruning of neural networks." Thesis, 2020. http://hdl.handle.net/1866/25592.

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Les réseaux de neurones sont une famille de modèles de l'apprentissage automatique qui sont capable d'apprendre des tâches complexes directement des données. Bien que produisant déjà des résultats impressionnants dans beaucoup de domaines tels que la reconnaissance de la parole, la vision par ordinateur ou encore la traduction automatique, il y a encore de nombreux défis dans l'entraînement et dans le déploiement des réseaux de neurones. En particulier, entraîner des réseaux de neurones nécessite typiquement d'énormes ressources computationnelles, et les modèles entraînés sont souvent trop gro

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Leszko, Dominika. "Time series forecasting for a call center in a Warsaw holding company." Master's thesis, 2020. http://hdl.handle.net/10362/102939.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>In recent years, artificial intelligence and cognitive technologies are actively being adopted in industries that use conversational marketing. Workforce managers face the constant challenge of balancing the priorities of service levels and related service costs. This problem is especially common when inaccurate forecasts lead to inefficient scheduling decisions and in turn result in dramatic impact on the customer engagement and experience and thus call center’s

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(11048391), Hao Sha. "SOLVING PREDICTION PROBLEMS FROM TEMPORAL EVENT DATA ON NETWORKS." Thesis, 2021.

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<div><div><div><p>Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of

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Savard, François. "Réseaux de neurones à relaxation entraînés par critère d'autoencodeur débruitant." Thèse, 2011. http://hdl.handle.net/1866/6176.

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L’apprentissage machine est un vaste domaine où l’on cherche à apprendre les paramètres de modèles à partir de données concrètes. Ce sera pour effectuer des tâches demandant des aptitudes attribuées à l’intelligence humaine, comme la capacité à traiter des don- nées de haute dimensionnalité présentant beaucoup de variations. Les réseaux de neu- rones artificiels sont un exemple de tels modèles. Dans certains réseaux de neurones dits profonds, des concepts "abstraits" sont appris automatiquement. Les travaux présentés ici prennent leur inspiration de réseaux de neurones profonds, de réseaux réc

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Straková, Jana. "Rozpoznávání pojmenovaných entit pomocí neuronových sítí." Doctoral thesis, 2017. http://www.nusl.cz/ntk/nusl-368176.

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Title: Neural Network Based Named Entity Recognition Author: Jana Straková Institute: Institute of Formal and Applied Linguistics Supervisor of the doctoral thesis: prof. RNDr. Jan Hajič, Dr., Institute of Formal and Applied Linguistics Abstract: Czech named entity recognition (the task of automatic identification and classification of proper names in text, such as names of people, locations and organizations) has become a well-established field since the publication of the Czech Named Entity Corpus (CNEC). This doctoral thesis presents the author's research of named entity recognition, mainly

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Dutil, Francis. "Prédiction et génération de données structurées à l'aide de réseaux de neurones et de décisions discrètes." Thèse, 2018. http://hdl.handle.net/1866/22124.

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