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Molter, Colin. "Storing information through complex dynamics in recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211039.
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The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, 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 net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in the desired item's basin of attraction. The second view shared by neural net researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The last assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli and being able to easily switch from one of these potential attractors to another in response to any coming stimulus.In this thesis, it is shown experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the back, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause but the consequence of the learning. However, it appears as an helpful consequence that widens the net's encoding capacity. To learn the information to be stored, an unsupervised Hebbian learning algorithm is introduced. By leaving the semantics of the attractors to be associated with the feeding data unprescribed, promising results have been obtained in term of storing capacity.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
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Vincent-Lamarre, Philippe. "Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39960.
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Many living organisms have the ability to execute complex behaviors and cognitive processes that are reliable. In many cases, such tasks are generated in the absence of an ongoing external input that could drive the activity on their underlying neural populations. For instance, writing the word "time" requires a precise sequence of muscle contraction in the hand and wrist. There has to be some patterns of activity in the areas of the brain responsible for this behaviour that are endogenously generated every time an individual performs this action. Whereas the question of how such neural code is transformed in the target motor sequence is a question of its own, their origin is perhaps even more puzzling. Most models of cortical and sub-cortical circuits suggest that many of their neural populations are chaotic. This means that very small amounts of noise, such as an additional action potential in a neuron of a network, can lead to completely different patterns of activity. Reservoir computing is one of the first frameworks that provided an efficient solution for biologically relevant neural networks to learn complex temporal tasks in the presence of chaos. We showed that although reservoirs (i.e. recurrent neural networks) are robust to noise, they are extremely sensitive to some forms of structural perturbations, such as removing one neuron out of thousands. We proposed an alternative to these models, where the source of autonomous activity is no longer originating from the reservoir, but from a set of oscillating networks projecting to the reservoir. In our simulations, we show that this solution produce rich patterns of activity and lead to networks that are both resistant to noise and structural perturbations. The model can learn a wide variety of temporal tasks such as interval timing, motor control, speech production and spatial navigation.
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Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.
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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The frameworks offer generative solutions for analyzing complex and irregular data structures without prior knowledge of data.
The first part of the dissertation proposes a novel method that simultaneously filters point cloud noise and outliers as well as completing missing data by utilizing a unified framework consisting of a novel tensor data representation, an adaptive feature encoder, and a generative Bayesian network. In the next section, a novel multi-level generative chaotic Recurrent Neural Network (RNN) has been proposed using a sparse tensor structure for image restoration. In the last part of the dissertation, we discuss the detection followed by localization, where we discuss extracting features from sparse tensors for data retrieval.Doctor of Philosophy
The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.
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Clodong, Sébastien. "Recurrent outbreaks in ecology : chaotic dynamics in complex networks." Phd thesis, Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2005/171/.
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Gegenstand der Dissertation ist die Untersuchung von wiederkehrenden Ausbrüchen (wie z.B. Epidemien) in der Natur. Dies gelang anhand von Modellen, die die Dynamik von Phytoplankton und die Ausbreitung von Krankheiten zwischen Städten beschreiben. Diese beide Systeme bilden hervorragende Beispiele für solche Phänomene.
Die Frage, ob die in der Zeit wiederkehrenden Ausbrüche ein Ausdruck chaotischer Dynamik sein können, ist aktuell in der Ökologie und fasziniert Wissenschaftler dieser Disziplin. Wir konnten zeigen, dass sich das Plankton-Modell im Falle von periodischem Antreiben über die Nährstoffe in einem chaotischen Regime befindet. Diese Dynamik wurde als die komplexe Wechselwirkung zweier Oszillatoren verstanden.
Ebenfalls wurde die Ausbreitung von Epidemien in Netzwerken wechselwirkender Städte mit unterschiedlichen Grössen untersucht. Dafür wurde zunächst die Kopplung zwischen zwei Städten als Verhältnis der Stadtgrössen eingeführt. Es konnte gezeigt werden, dass das System sich in einem globalen zweijährigen Zyklus, der auch in den realen Daten beobachtet wird, befinden kann.
Der Effekt von Heterogenität in der Grösseverteilung ist durch gewichtete Kopplung von generischen Modellen (Zelt- und Logistische Abbildung) in Netzwerken im Detail untersucht worden. Eine neue Art von Kopplungsfunktion mit nichtlinearer Sättigung wurde eingeführt, um die Stabilität des Systems zu gewährleisten. Diese Kopplung beinhaltet einen Parameter, der es erlaubt, die Netzwerktopologie von globaler Kopplung in gerichtete Netzwerke gleichmässig umzuwandeln. Die Dynamik des Systems wurde anhand von Bifurkationsdiagrammen untersucht. Zum Verständnis dieser Dynamik wurde eine effektive Theorie, die die beobachteten Bifurkationen sehr gut nachahmt, entwickelt.One of the most striking features of ecological systems is their ability to undergo sudden outbreaks in the population numbers of one or a small number of species. The similarity of outbreak characteristics, which is exhibited in totally different and unrelated (ecological) systems naturally leads to the question whether there are universal mechanisms underlying outbreak dynamics in Ecology. It will be shown into two case studies (dynamics of phytoplankton blooms under variable nutrients supply and spread of epidemics in networks of cities) that one explanation for the regular recurrence of outbreaks stems from the interaction of the natural systems with periodical variations of their environment. Natural aquatic systems like lakes offer very good examples for the annual recurrence of outbreaks in Ecology. The idea whether chaos is responsible for the irregular heights of outbreaks is central in the domain of ecological modeling. This question is investigated in the context of phytoplankton blooms. The dynamics of epidemics in networks of cities is a problem which offers many ecological and theoretical aspects. The coupling between the cities is introduced through their sizes and gives rise to a weighted network which topology is generated from the distribution of the city sizes. We examine the dynamics in this network and classified the different possible regimes. It could be shown that a single epidemiological model can be reduced to a one-dimensional map. We analyze in this context the dynamics in networks of weighted maps. The coupling is a saturation function which possess a parameter which can be interpreted as an effective temperature for the network. This parameter allows to vary continously the network topology from global coupling to hierarchical network. We perform bifurcation analysis of the global dynamics and succeed to construct an effective theory explaining very well the behavior of the system.
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Clodong, Sébastien. "Recurrent outbreaks in ecology chaotic dynamics in complex networks /." [S.l. : s.n.], 2004. http://pub.ub.uni-potsdam.de/2004/0062/clodong.pdf.
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Żbikowski, Rafal Waclaw. "Recurrent neural networks some control aspects /." Connect to electronic version, 1994. http://hdl.handle.net/1905/180.
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Ahamed, Woakil Uddin. "Quantum recurrent neural networks for filtering." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.
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The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger waveequation (SWE) plays the key part. This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of the SWE. Theincorporation of SWE into the field of neural network provides a framework which is socalled the quantum recurrent neural network (QRNN). A filter based on this approachis categorized as intelligent filter, as the underlying formulation is based on the analogyto real neuron.In a QRNN filter, the interaction between the observed signal and the wave dynamicsare governed by the SWE. A key issue, therefore, is achieving a solution of the SWEthat ensures the stability of the numerical scheme. Another important aspect indesigning this filter is in the way the wave function transforms the observed signalthrough the network. This research has shown that there are two different ways (anormal wave and a calm wave, Chapter-5) this transformation can be achieved and thesewave packets play a critical role in the evolution of the pdf. In this context, this thesishave investigated the following issues: existing filtering approach in the evolution of thepdf, architecture of the QRNN, the method of solving SWE, numerical stability of thesolution, and propagation of the waves in the well. The methods developed in this thesishave been tested with relevant simulations. The filter has also been tested with somebenchmark chaotic series along with applications to real world situation. Suggestionsare made for the scope of further developments.
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Zbikowski, Rafal Waclaw. "Recurrent neural networks : some control aspects." Thesis, University of Glasgow, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390233.
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Jacobsson, Henrik. "Rule extraction from recurrent neural networks." Thesis, University of Sheffield, 2006. http://etheses.whiterose.ac.uk/6081/.
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Bonato, Tommaso. "Time Series Predictions With Recurrent Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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L'obiettivo principale di questa tesi è studiare come gli algoritmi di apprendimento automatico (machine learning in inglese) e in particolare le reti neurali LSTM (Long Short Term Memory) possano essere utilizzati per prevedere i valori futuri di una serie storica regolare come, per esempio, le funzioni seno e coseno. Una serie storica è definita come una sequenza di osservazioni s_t ordinate nel tempo.
Inoltre cercheremo di applicare gli stessi principi per prevedere i valori di una serie storica prodotta utilizzando i dati di vendita di un prodotto cosmetico durante un periodo di tre anni.
Prima di arrivare alla parte pratica di questa tesi è necessario introdurre alcuni concetti fondamentali che saranno necessari per sviluppare l'architettura e il codice del nostro modello.
Sia nell'introduzione teorica che nella parte pratica l'attenzione sarà focalizzata sull'uso di RNN (Recurrent Neural Network o Rete Neurale Ricorrente) poiché sono le reti neurali più adatte a questo tipo di problema. Un particolare tipo di RNN, chiamato Long Short Term Memory (LSTM), sarà soggetto dello studio principale di questa tesi e verrà presentata e utilizzata anche una delle sue varianti chiamata Gated Recurrent Unit (GRU).
Questa tesi, in conclusione, conferma che LSTM e GRU sono il miglior tipo di rete neurale per le previsioni di serie temporali. Nell'ultima parte analizzeremo le differenze tra l'utilizzo di una CPU e una GPU durante la fase di training della rete neurale.
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Silfa, Franyell. "Energy-efficient architectures for recurrent neural networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/671448.
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Deep Learning algorithms have been remarkably successful in applications such as Automatic Speech Recognition and Machine Translation. Thus, these kinds of applications are ubiquitous in our lives and are found in a plethora of devices. These algorithms are composed of Deep Neural Networks (DNNs), such as Convolutional Neural Networks and Recurrent Neural Networks (RNNs), which have a large number of parameters and require a large amount of computations. Hence, the evaluation of DNNs is challenging due to their large memory and power requirements.
RNNs are employed to solve sequence to sequence problems such as Machine Translation. They contain data dependencies among the executions of time-steps hence the amount of parallelism is severely limited. Thus, evaluating them in an energy-efficient manner is more challenging than evaluating other DNN algorithms.
This thesis studies applications using RNNs to improve their energy efficiency on specialized architectures. Specifically, we propose novel energy-saving techniques and highly efficient architectures tailored to the evaluation of RNNs. We focus on the most successful RNN topologies which are the Long Short Term memory and the Gated Recurrent Unit.
First, we characterize a set of RNNs running on a modern SoC. We identify that accessing the memory to fetch the model weights is the main source of energy consumption. Thus, we propose E-PUR: an energy-efficient processing unit for RNN inference. E-PUR achieves 6.8x speedup and improves energy consumption by 88x compared to the SoC. These benefits are obtained by improving the temporal locality of the model weights.
In E-PUR, fetching the parameters is the main source of energy consumption. Thus, we strive to reduce memory accesses and propose a scheme to reuse previous computations. Our observation is that when evaluating the input sequences of an RNN model, the output of a given neuron tends to change lightly between consecutive evaluations.Thus, we develop a scheme that caches the neurons' outputs and reuses them whenever it detects that the change between the current and previously computed output value for a given neuron is small avoiding to fetch the weights. In order to decide when to reuse a previous value we employ a Binary Neural Network (BNN) as a predictor of reusability. The low-cost BNN can be employed in this context since its output is highly correlated to the output of RNNs. We show that our proposal avoids more than 24.2% of computations. Hence, on average, energy consumption is reduced by 18.5% for a speedup of 1.35x.
RNN models’ memory footprint is usually reduced by using low precision for evaluation and storage. In this case, the minimum precision used is identified offline and it is set such that the model maintains its accuracy. This method utilizes the same precision to compute all time-steps.Yet, we observe that some time-steps can be evaluated with a lower precision while preserving the accuracy. Thus, we propose a technique that dynamically selects the precision used to compute each time-step. A challenge of our proposal is choosing a lower bit-width. We address this issue by recognizing that information from a previous evaluation can be employed to determine the precision required in the current time-step. Our scheme evaluates 57% of the computations on a bit-width lower than the fixed precision employed by static methods. We implement it on E-PUR and it provides 1.46x speedup and 19.2% energy savings on average.Los algoritmos de aprendizaje profundo han tenido un éxito notable en aplicaciones como el reconocimiento automático de voz y la traducción automática. Por ende, estas aplicaciones son omnipresentes en nuestras vidas y se encuentran en una gran cantidad de dispositivos. Estos algoritmos se componen de Redes Neuronales Profundas (DNN), tales como las Redes Neuronales Convolucionales y Redes Neuronales Recurrentes (RNN), las cuales tienen un gran número de parámetros y cálculos. Por esto implementar DNNs en dispositivos móviles y servidores es un reto debido a los requisitos de memoria y energía. Las RNN se usan para resolver problemas de secuencia a secuencia tales como traducción automática. Estas contienen dependencias de datos entre las ejecuciones de cada time-step, por ello la cantidad de paralelismo es limitado. Por eso la evaluación de RNNs de forma energéticamente eficiente es un reto. En esta tesis se estudian RNNs para mejorar su eficiencia energética en arquitecturas especializadas. Para esto, proponemos técnicas de ahorro energético y arquitecturas de alta eficiencia adaptadas a la evaluación de RNN. Primero, caracterizamos un conjunto de RNN ejecutándose en un SoC. Luego identificamos que acceder a la memoria para leer los pesos es la mayor fuente de consumo energético el cual llega hasta un 80%. Por ende, creamos E-PUR: una unidad de procesamiento para RNN. E-PUR logra una aceleración de 6.8x y mejora el consumo energético en 88x en comparación con el SoC. Esas mejoras se deben a la maximización de la ubicación temporal de los pesos. En E-PUR, la lectura de los pesos representa el mayor consumo energético. Por ende, nos enfocamos en reducir los accesos a la memoria y creamos un esquema que reutiliza resultados calculados previamente. La observación es que al evaluar las secuencias de entrada de un RNN, la salida de una neurona dada tiende a cambiar ligeramente entre evaluaciones consecutivas, por lo que ideamos un esquema que almacena en caché las salidas de las neuronas y las reutiliza cada vez que detecta un cambio pequeño entre el valor de salida actual y el valor previo, lo que evita leer los pesos. Para decidir cuándo usar un cálculo anterior utilizamos una Red Neuronal Binaria (BNN) como predictor de reutilización, dado que su salida está altamente correlacionada con la salida de la RNN. Esta propuesta evita más del 24.2% de los cálculos y reduce el consumo energético promedio en 18.5%. El tamaño de la memoria de los modelos RNN suele reducirse utilizando baja precisión para la evaluación y el almacenamiento de los pesos. En este caso, la precisión mínima utilizada se identifica de forma estática y se establece de manera que la RNN mantenga su exactitud. Normalmente, este método utiliza la misma precisión para todo los cálculos. Sin embargo, observamos que algunos cálculos se pueden evaluar con una precisión menor sin afectar la exactitud. Por eso, ideamos una técnica que selecciona dinámicamente la precisión utilizada para calcular cada time-step. Un reto de esta propuesta es como elegir una precisión menor. Abordamos este problema reconociendo que el resultado de una evaluación previa se puede emplear para determinar la precisión requerida en el time-step actual. Nuestro esquema evalúa el 57% de los cálculos con una precisión menor que la precisión fija empleada por los métodos estáticos. Por último, la evaluación en E-PUR muestra una aceleración de 1.46x con un ahorro de energía promedio de 19.2%
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Brax, Christoffer. "Recurrent neural networks for time-series prediction." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-480.
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Recurrent neural networks have been used for time-series prediction with good results. In this dissertation recurrent neural networks are compared with time-delayed feed forward networks, feed forward networks and linear regression models on a prediction task. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns. It is evaluated if more accurate predictions can be made when only using the campaign segments of the data.
Throughout the entire project a knowledge discovery process, identified in the literature has been used to give a structured work-process. The results show that the recurrent network is not better than the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also show that more accurate predictions could be made when only using information from campaign segments.
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Rabi, Gihad. "Visual speech recognition by recurrent neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0010/MQ36169.pdf.
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Miller, Paul Ian. "Recurrent neural networks and adaptive motor control." Thesis, University of Stirling, 1997. http://hdl.handle.net/1893/21520.
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This thesis is concerned with the use of neural networks for motor control tasks. The main goal of the thesis is to investigate ways in which the biological notions of motor programs and Central Pattern Generators (CPGs) may be implemented in a neural network framework. Biological CPGs can be seen as components within a larger control scheme, which is basically modular in design. In this thesis, these ideas are investigated through the use of modular recurrent networks, which are used in a variety of control tasks. The first experimental chapter deals with learning in recurrent networks, and it is shown that CPGs may be easily implemented using the machinery of backpropagation. The use of these CPGs can aid the learning of pattern generation tasks; they can also mean that the other components in the system can be reduced in complexity, say, to a purely feedforward network. It is also shown that incremental learning, or 'shaping' is an effective method for building CPGs. Genetic algorithms are also used to build CPGs; although computational effort prevents this from being a practical method, it does show that GAs are capable of optimising systems that operate in the context of a larger scheme. One interesting result from the GA is that optimal CPGs tend to have unstable dynamics, which may have implications for building modular neural controllers. The next chapter applies these ideas to some simple control tasks involving a highly redundant simulated robot arm. It was shown that it is relatively straightforward to build CPGs that represent elements of pattern generation, constraint satisfaction. and local feedback. This is indirect control, in which errors are backpropagated through a plant model, as well as the ePG itself, to give errors for the controller. Finally, the third experimental chapter takes an alternative approach, and uses direct control methods, such as reinforcement learning. In reinforcement learning, controller outputs have unmodelled effects; this allows us to build complex control systems, where outputs modulate the couplings between sets of dynamic systems. This was shown for a simple case, involving a system of coupled oscillators. A second set of experiments investigates the use of simplified models of behaviour; this is a reduced form of supervised learning, and the use of such models in control is discussed.
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Graves, Alex. "Supervised sequence labelling with recurrent neural networks." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/673554/673554.pdf.
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Xie, Xiaohui 1972. "Dynamics and learning in recurrent neural networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8393.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2002.Includes bibliographical references (p. 141-151).
This thesis is a study of dynamics and learning in recurrent neural networks. Many computations of neural systems are carried out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the network's function. In this thesis, I aim at studying several recurrent network models and relating the dynamics with the networks' computation. For this purpose, three systems are studied and analyzed in detail: The first one is a network model for direction selectivity; the second one is a generalized network of Winner-Take-All; the third one is a model for integration in head-direction systems. One distinctive feature of neural systems is the ability of learning. The other part of my thesis is on learning in biologically motivated neural networks. Specifically, I study how the spike-time-dependent synaptic plasticity helps to stabilize persistent neural activities in the ocular motor integrator. I study the connections between back-propagation and contrastive-Hebbian learning, and show how backpropagation could be equivalently implemented by contrastive-Hebbian learning in a layered network. I also propose a learning rule governing synaptic plasticity in a network of spiking neurons and compare it with recent experimental results on spike-time-dependent plasticity.
by Xiaohui Xie.
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Besharat, Pour Shiva. "Hierarchical sales forecasting using Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290892.
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Sales forecasting equips businesses with the essential basis for planning future investments, controlling costs, and production. This research is in cooperation with a property development company for the purpose of improving the accuracy of manual sales forecasting. The objective is to investigate the effects of using the underlying factors that affect the individual sales of the company in forecasting the company’s income. One approach uses an aggregation of the estimates of the individual sales to approximate the company’s income. This approach uses the underlying hierarchical factors of the company’s individual sales to forecast future sales, which is known as the bottom-up approach. Another approach, known as the direct approach, uses the history of the company’s income instead. The bottom-up approach estimates the income of the company in the chosen target quarter, Q4 2019, with a percentage error of 33 percent. On the contrary, the direct approach provides an estimate of the company’s income inQ4 2019 with a percentage error of 3 percent. The strength of the bottom-up approach is in providing detailed forecasts of the individual sales of the company. The direct approach, however, is more convenient in learning the overall behavior of the company’s earnings.Försäljningsprognoser ger företag förutsättningar för planering av framtida investeringar och kontroll av både kostnader och produktion. Denna forskning har skett i samarbete med ett fastighetsutvecklingsföretag i syfte att förbättra noggrannheten i manuell försäljningsprognostisering. Målet är att undersöka effekterna av att använda de bakomliggande faktorer som påverkar enskild försäljning i prognoser för företagets intäkter. Ett av tillvägagångssätten som undersöks använder en sammanstallning av enskilda historiska försäljningar för att förutse företagets kommande intäkter. Detta tillvägagångssätt använder de bakomliggande hierarkiska faktorerna för företagets individuella försäljning för att prognostisera framtida försäljning, och metoden är känd som botten-upp-metoden. Ett annat tillvägagångssätt, känt som direktmetoden, använder företagets historiska inkomster som data i stället. Botten-upp-metoden användes för att upp- skatta företagets intäkter under Q4 2019 och gav ett procentuellt fel på 33 pro- cent. Direktmetoden, ˚a andra sidan, gav en uppskattning av företagets intäkter under Q4 2019 med ett procentuellt fel på 3 procent. Styrkan med botten- upp-metoden ¨ar att den kan tillhandahålla detaljerade prognoser för företagets individuella försäljning, samtidigt som direktmetoden ¨ar mer praktisk för att uppskatta företagets totala inkomster.
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Alam, Samiul. "Recurrent neural networks in electricity load forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233254.
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In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting. The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. The second study compares FFNNs and LSTMs of different complexities (i.e. network sizes) when restrictions imposed by limitations of the real world are taken into consideration. No significant differences are found between the predictive performances of the two neural network approaches. However, adding air temperature as extra input to the LSTM is found to significantly decrease its performance. Furthermore, the predictive performance of the FFNN is found to significantly decrease as the network complexity grows, while the predictive performance of the LSTM is found to increase as the network complexity grows. All the findings considered, we do not find that there is enough evidence in favour of the LSTM in electricity load forecasting.I denna uppsats beskrivs två studier som jämför feed-forward neurala nätverk (FFNN) och long short-term memory neurala nätverk (LSTM) i prognostisering av elkonsumtion. I den första studien undersöks univariata modeller som använder tidigare elkonsumtion, och flervariata modeller som använder tidigare elkonsumtion och temperaturmätningar, för att göra prognoser av elkonsumtion för nästa dag. Hur långt bak i tiden tidigare information hämtas ifrån samt upplösningen av tidigare information varieras. I den andra studien undersöks FFNN- och LSTM-modeller med praktiska begränsningar såsom tillgänglighet av data i åtanke. Även storleken av nätverken varieras. I studierna finnes ingen skillnad mellan FFNN- och LSTM-modellernas förmåga att prognostisera elkonsumtion. Däremot minskar FFNN-modellens förmåga att prognostisera elkonsumtion då storleken av modellen ökar. Å andra sidan ökar LSTM-modellens förmåga då storkelen ökar. Utifrån dessa resultat anser vi inte att det finns tillräckligt med bevis till förmån för LSTM-modeller i prognostisering av elkonsumtion.
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Tegnér, Gustaf. "Recurrent neural networks for financial asset forecasting." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229924.
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The application of neural networks in finance has found renewed interest in the past few years. Neural networks have a proven capability of modeling non-linear relationships and have been proven widely successful in domains such as image and speech recognition. These favorable properties of the Neural Network make them an alluring choice of model when studying the financial markets. This thesis is concerned with investigating the use of recurrent neural networks for predicting future financial asset price movements on a set of futures contracts. To aid our research, we compare them to a set of simple feed-forward networks. We conduct further research into the various networks by considering different objective loss functions and how they affect our networks performance. This discussion is extended by considering multi-loss networks as well. The use of different loss functions sheds light on the importance of feature selection. We study a set of simple and complex features and how they affect our model. This aids us in further examining the difference between our networks. Lastly, we analyze of the gradients of our model to provide additional insight into the properties of our features. Our results show that recurrent networks provide superior predictive performance compared to feed-forward networks both when evaluating the Sharpe ratio and accuracy. The simple features show better results when optimizing for accuracy. When the network aims to maximize Sharpe, the complex features are preferred. The use of multi-loss networks proved successful when we consider achieving a high Sharpe ratio as our main objective. Our results show significant improved performance compared to a set of simple benchmarks. Through ensemble methods, we achieve a Sharpe ratio of 1.44 and an accuracy of 52.77% on the test setTillämpningen av neurala nätverk i finans har fått förnyat intresse under de senaste åren. Neurala nätverk har en erkänd förmåga att kunna modellera icke-linjära förhållanden och har bevisligen visat sig användbara inom områden som bild och taligenkänning. Dessa egenskaper gör neurala nätverk till ett attraktivt val av model för att studera finansmarknaden Denna uppsats studerar användandet av rekurrenta neurala nätverk för pre-diktering av framtida prisrörelser av ett antal futures kontrakt. För att underlätta får analys jämför vi dessa nätverk med en uppsättning av enkla framåtkopplade nätverk. Vi dyker sedan djupare in i vår analys genom att jämföra olika målfunktioner för nätverken och hur de påverkar våra nätverks prestation. Vi utökar sedan den här diskussionen genom att också undersöka multi-förlust nätverk. Användandet av flera förlust funktioner visar på betydelsen av vårt urval av attribut från indatan. Vi studerar ett par simpla och komplexa attribut och hur de påverkar vår modell. Det hjälper oss att göra en ytterligare jämförelse mellan våra nätverk. Avslutningsvis så undersöker vi vår modells gradienter för att få en utökad förståelse över hur vår modell agerar med olika attribut. Resultaten visar på att rekurrenta nätverk utpresterar framåtkopplade nät-verk, både i uppgiften att maximera sharpe ration och precision. De enkla attributen visar på bättre resultat när nätverket optimeras för precision. När vi optimerar för att maximera Sharpe ration fungerar de komplexa attributen bättre. Tillämpningen av multi-förlust nätverk visade sig framgångsrik när vårt huvudmål var at maximera sharpe ration. Våra resultat visar på en signifikant ökad prestation av våra nätverk jämfört med ett par enkla benchmarks. Genom ensemble metoder uppnår vi en Sharpe ratio på 1.44 samt en precision på 52.77% på test datan.
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Perumal, Subramoniam. "Stability and Switchability in Recurrent Neural Networks." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227194814.
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Ljungehed, Jesper. "Predicting Customer Churn Using Recurrent Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210670.
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Churn prediction is used to identify customers that are becoming less loyal and is an important tool for companies that want to stay competitive in a rapidly growing market. In retail, a dynamic definition of churn is needed to identify churners correctly. Customer Lifetime Value (CLV) is the monetary value of a customer relationship. No change in CLV for a given customer indicates a decrease in loyalty. This thesis proposes a novel approach to churn prediction. The proposed model uses a Recurrent Neural Network to identify churners based on Customer Lifetime Value time series regression. The results show that the model performs better than random. This thesis also investigated the use of the K-means algorithm as a replacement to a rule-extraction algorithm. The K-means algorithm contributed to a more comprehensive analytical context regarding the churn prediction of the proposed model.Illojalitet prediktering används för att identifiera kunder som är påväg att bli mindre lojala och är ett hjälpsamt verktyg för att ett företag ska kunna driva en konkurrenskraftig verksamhet. I detaljhandel behöves en dynamisk definition av illojalitet för att korrekt kunna identifera illojala kunder. Kundens livstidsvärde är ett mått på monetärt värde av en kundrelation. En avstannad förändring av detta värde indikerar en minskning av kundens lojalitet. Denna rapport föreslår en ny metod för att utföra illojalitet prediktering. Den föreslagna metoden består av ett återkommande neuralt nätverk som används för att identifiera illojalitet hos kunder genom att prediktera kunders livstidsvärde. Resultaten visar att den föreslagna modellen presterar bättre jämfört med slumpmässig metod. Rapporten undersöker också användningen av en k-medelvärdesalgoritm som ett substitut för en regelextraktionsalgoritm. K-medelsalgoritm bidrog till en mer omfattande analys av illojalitet predikteringen.
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Potter, Chris, Kurt Kosbar, and Adam Panagos. "MIMO Channel Prediction Using Recurrent Neural Networks." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606193.
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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, CaliforniaAdaptive modulation is a communication technique capable of maximizing throughput while guaranteeing a fixed symbol error rate (SER). However, this technique requires instantaneous channel state information at the transmitter. This can be obtained by predicting channel states at the receiver and feeding them back to the transmitter. Existing algorithms used to predict single-input single-output (SISO) channels with recurrent neural networks (RNN) are extended to multiple-input multiple-output (MIMO) channels for use with adaptive modulation and their performance is demonstrated in several examples.
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Berlati, Alessandro. "Ambiguity in Recurrent Models: Predicting Multiple Hypotheses with Recurrent Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16611/.
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Multiple Hypothesis Prediction (MHP) models have been introduced to deal with uncertainty in feedforward neural networks, in particular it has been shown how to easily convert a standard single-prediction neural network into one able to show many feasible outcomes.
Ambiguity, however, is present also in problems where feedback model are needed, such as sequence generation and time series classification. In our work, we propose an extension of MHP to Recurrent Neural Networks (RNNs), especially those consisting of Long Short-Term Memory units. We test the resulting models on both regression and classification problems using public datasets, showing promising results. Our way to build MHP models can be used to retrofit other works, leading the way towards further research.
We can find many possible application scenarios in the autonomous driv- ing environment. For example, trajectory prediction, for humans and cars, or intention classification (e.g. lane change detection) are both tasks where ambiguous situations are frequent.
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Sarti, Paolo. "Embeddings for text classification with recurrent neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Find full textAbstract:
L'importanza di metodi automatici per la classificazione ed estrazione di informazioni da testi è cresciuta significativamente negli ultimi anni, a causa della produzione sempre maggiore di questo tipo di dati, specialmente tramite piattaforme web. Questo ha portato allo sviluppo di nuovi algoritmi per analizzare testi non strutturati. Le tecniche di "Embedding", che associano parole o parti di testo di lunghezza variabile a vettori di dimensione fissa mantenendo relazioni di similarità semantica, sono state un grande progresso per il campo del "Natural Language Processing". Inoltre, avanzamenti nelle tecniche di Deep Learning hanno migliorato significativamente la classificazione del testo, grazie agli affinamenti delle architetture delle reti neurali ricorrenti, in grado di processare sequenze di dimensioni variabili.
Lo scopo di questo lavoro è stata la realizzazione di un prototipo che utilizzasse le tecniche citate per classificare documenti ed estrarre parti di testo. Il dominio di riferimento era composto da documenti amministrativi redatti da notai. Per la classificazione sono state utilizzate reti ricorrenti di tipo LSTM, e due tipologie di "embedding": a livello di parole ed a livello di frase. La prima tecnica è risultata più performante sull'insieme dei documenti di test, raggiungendo il 98,8% di accuratezza, mentre la seconda si è fermata al 96,7%.
L'estrazione di parti rilevanti del testo è stata impostata come un problema di classificazione multi classe a livello della singola frase, utilizzando "word embedding" e reti ricorrenti LSTM. Complessivamente l'accuratezza ha raggiunto l'85,5% sull'insieme di test, mostrando però risultati non uniformi sulle singole classi. Tuttavia, si è rilevata una confusione ridotta tra le classi che rappresentavano le informazioni da estrarre. I modelli predittivi sono stati integrati in un prototipo, che ha permesso di verificare anche qualitativamente le buone prestazioni delle tecniche impiegate.
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Gers, Félix. "Long short-term memory in recurrent neural networks /." [S.l.] : [s.n.], 2001. http://library.epfl.ch/theses/?nr=2366.
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Tino, Peter, and Georg Dorffner. "Recurrent neural networks with iterated function systems dynamics." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/948/1/document.pdf.
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We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley [1] as a fractal image compression mechanism. The key idea is that 1) in our model we avoid learning the RNN state part by having non-trainable connections between the context and recurrent layers (this makes the training process less problematic and faster), 2) the RNN state part codes the information processing states in the symbolic input stream in a well-organized and intuitively appealing way. We show that there is a direct correspondence between the Rényi entropy spectra characterizing the input stream and the spectra of Renyi generalized dimensions of activations inside the RNN state space. We test both the new RNN model with IFS dynamics and its conventional counterpart with trainable recurrent part on two chaotic symbolic sequences. In our experiments, RNNs with IFS dynamics outperform the conventional RNNs with respect to information theoretic measures computed on the training and model generated sequences. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Steinberger, Thomas, and Lucas Zinner. "Complete controllability of discrete-time recurrent neural networks." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/440/1/document.pdf.
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This paper presents a characterization of complete controllability for the class of discrete-time recurrent neural networks. We prove that complete controllability holds if and only if the rank of the control matrix equals the state space dimension. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Mastrogiuseppe, Francesca. "From dynamics to computations in recurrent neural networks." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE048/document.
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Le cortex cérébral des mammifères est constitué de larges et complexes réseaux de neurones. La tâche de ces assemblées de cellules est d’encoder et de traiter, le plus précisément possible, l'information sensorielle issue de notre environnement extérieur. De façon surprenante, les enregistrements électrophysiologiques effectués sur des animaux en comportement ont montré que l’activité corticale est excessivement irrégulière. Les motifs temporels d’activité ainsi que les taux de décharge moyens des cellules varient considérablement d’une expérience à l’autre, et ce malgré des conditions expérimentales soigneusement maintenues à l’identique. Une hypothèse communément répandue suggère qu'une partie importante de cette variabilité émerge de la connectivité récurrente des réseaux. Cette hypothèse se fonde sur la modélisation des réseaux fortement couplés. Une étude classique [Sompolinsky et al, 1988] a en effet montré qu'un réseau de cellules aux connections aléatoires exhibe une transition de phase : l’activité passe d'un point fixe ou le réseau est inactif, à un régime chaotique, où les taux de décharge des cellules fluctuent au cours du temps et d’une cellule à l’autre. Ces analyses soulèvent néanmoins de nombreuse questions : de telles fluctuations sont-elles encore visibles dans des réseaux corticaux aux architectures plus réalistes? De quelle façon cette variabilité intrinsèque dépend-elle des paramètres biophysiques des cellules et de leurs constantes de temps ? Dans quelle mesure de tels réseaux chaotiques peuvent-ils sous-tendre des computations ? Dans cette thèse, on étudiera la dynamique et les propriétés computationnelles de modèles de circuits de neurones à l’activité hétérogène et variable. Pour ce faire, les outils mathématiques proviendront en grande partie des systèmes dynamiques et des matrices aléatoires. Ces approches seront couplées aux méthodes statistiques des champs moyens développées pour la physique des systèmes désordonnées. Dans la première partie de cette thèse, on étudiera le rôle de nouvelles contraintes biophysiques dans l'apparition d’une activité irrégulière dans des réseaux de neurones aux connections aléatoires. Dans la deuxième et la troisième partie, on analysera les caractéristiques de cette variabilité intrinsèque dans des réseaux partiellement structurées supportant des calculs simples comme la prise de décision ou la création de motifs temporels. Enfin, inspirés des récents progrès dans le domaine de l’apprentissage statistique, nous analyserons l’interaction entre une architecture aléatoire et une structure de basse dimension dans la dynamique des réseaux non-linéaires. Comme nous le verrons, les modèles ainsi obtenus reproduisent naturellement un phénomène communément observé dans des enregistrements électrophysiologiques : une dynamique de population de basse dimension combinée avec représentations neuronales irrégulières, à haute dimension, et mixtesThe mammalian cortex consists of large and intricate networks of spiking neurons. The task of these complex recurrent assemblies is to encode and process with high precision the sensory information which flows in from the external environment. Perhaps surprisingly, electrophysiological recordings from behaving animals have pointed out a high degree of irregularity in cortical activity. The patterns of spikes and the average firing rates change dramatically when recorded in different trials, even if the experimental conditions and the encoded sensory stimuli are carefully kept fixed. One current hypothesis suggests that a substantial fraction of that variability emerges intrinsically because of the recurrent circuitry, as it has been observed in network models of strongly interconnected units. In particular, a classical study [Sompolinsky et al, 1988] has shown that networks of randomly coupled rate units can exhibit a transition from a fixed point, where the network is silent, to chaotic activity, where firing rates fluctuate in time and across units. Such analysis left a large number of questions unsolved: can fluctuating activity be observed in realistic cortical architectures? How does variability depend on the biophysical parameters and time scales? How can reliable information transmission and manipulation be implemented with such a noisy code? In this thesis, we study the spontaneous dynamics and the computational properties of realistic models of large neural circuits which intrinsically produce highly variable and heterogeneous activity. The mathematical tools of our analysis are inherited from dynamical systems and random matrix theory, and they are combined with the mean field statistical approaches developed for the study of physical disordered systems. In the first part of the dissertation, we study how strong rate irregularities can emerge in random networks of rate units which obey some among the biophysical constraints that real cortical neurons are subject to. In the second and third part of the dissertation, we investigate how variability is characterized in partially structured models which can support simple computations like pattern generation and decision making. To this aim, inspired by recent advances in networks training techniques, we address how random connectivity and low-dimensional structure interact in the non-linear network dynamics. The network models that we derive naturally capture the ubiquitous experimental observations that the population dynamics is low-dimensional, while neural representations are irregular, high-dimensional and mixed
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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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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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Le, Ngan Thi Hoang. "Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1166.
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Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation, augmented reality systems, semantic searching, medical imaging are on the rise, requiring more accurate and efficient segmentation mechanisms. In recent years, deep learning approaches based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have dramatically emerged as the dominant paradigm for solving many problems in computer vision and machine learning. The main focus of this thesis is to investigate robust approaches that can tackle the challenging semantic labeling tasks including semantic instance segmentation and scene understanding. In the first approach, we convert the classic variational Level Set method to a learnable deep framework by proposing a novel definition of contour evolution named Recurrent Level Set (RLS). The proposed RLS employs Gated Recurrent Units to solve the energy minimization of a variational Level Set functional. The curve deformation processes in RLS is formulated as a hidden state evolution procedure and is updated by minimizing an energy functional composed of fitting forces and contour length. We show that by sharing the convolutional features in a fully end-to-end trainable framework, RLS is able to be extended to Contextual Recurrent Level Set (CRLS) Networks to address semantic segmentation in the wild problem. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational Level Set-based methods whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches on PAS CAL VOC 2012 and MS COCO 2014 databases. The second proposed approach, Contextual Recurrent Residual Networks (CRRN), inherits all the merits of sequence learning information and residual learning in order to simultaneously model long-range contextual infor- mation and learn powerful visual representation within a single deep network. Our proposed CRRN deep network consists of three parts corresponding to sequential input data, sequential output data and hidden state as in a recurrent network. Each unit in hidden state is designed as a combination of two components: a context-based component via sequence learning and a visualbased component via residual learning. That means, each hidden unit in our proposed CRRN simultaneously (1) learns long-range contextual dependencies via a context-based component. The relationship between the current unit and the previous units is performed as sequential information under an undirected cyclic graph (UCG) and (2) provides powerful encoded visual representation via residual component which contains blocks of convolution and/or batch normalization layers equipped with an identity skip connection. Furthermore, unlike previous scene labeling approaches [1, 2, 3], our method is not only able to exploit the long-range context and visual representation but also formed under a fully-end-to-end trainable system that effectively leads to the optimal model. In contrast to other existing deep learning networks which are based on pretrained models, our fully-end-to-end CRRN is completely trained from scratch. The experiments are conducted on four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford background, and SUN datasets, and compared against various state-of-the-art scene labeling methods.
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Mehta, Manish P. "Prediction of manufacturing operations sequence using recurrent neural networks." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1177089656.
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Vartak, Aniket Arun. "GAUSS-NEWTON BASED LEARNING FOR FULLY RECURRENT NEURAL NETWORKS." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4429.
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The thesis discusses a novel off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. Within the framework of the research presented, a new off-line and on-line variation of RTRL is presented, that is based on the Gauss-Newton method. The method itself is an approximate Newton's method tailored to the specific optimization problem, (non-linear least squares), which aims to speed up the process of FRNN training. The new approach stands as a robust and effective compromise between the original gradient-based RTRL (low computational complexity, slow convergence) and Newton-based variants of RTRL (high computational complexity, fast convergence). By gathering information over time in order to form Gauss-Newton search vectors, the new learning algorithm, GN-RTRL, is capable of converging faster to a better quality solution than the original algorithm. Experimental results reflect these qualities of GN-RTRL, as well as the fact that GN-RTRL may have in practice lower computational cost in comparison, again, to the original RTRL.M.S.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Electrical and Computer Engineering
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Senior, Andrew William. "Off-line cursive handwriting recognition using recurrent neural networks." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338024.
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Fors, Johansson Christoffer. "Arrival Time Predictions for Buses using Recurrent Neural Networks." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165133.
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In this thesis, two different types of bus passengers are identified. These two types, namely current passengers and passengers-to-be have different needs in terms of arrival time predictions. A set of machine learning models based on recurrent neural networks and long short-term memory units were developed to meet these needs. Furthermore, bus data from the public transport in Östergötland county, Sweden, were collected and used for training new machine learning models. These new models are compared with the current prediction system that is used today to provide passengers with arrival time information. The models proposed in this thesis uses a sequence of time steps as input and the observed arrival time as output. Each input time step contains information about the current state such as the time of arrival, the departure time from thevery first stop and the current position in Cartesian coordinates. The targeted value for each input is the arrival time at the next time step. To predict the rest of the trip, the prediction for the next step is simply used as input in the next time step. The result shows that the proposed models can improve the mean absolute error per stop between 7.2% to 40.9% compared to the system used today on all eight routes tested. Furthermore, the choice of loss function introduces models thatcan meet the identified passengers need by trading average prediction accuracy for a certainty that predictions do not overestimate or underestimate the target time in approximately 95% of the cases.
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Alvarez, Mouravskaia Kevin. "Metaphor identification for Spanish sentences using recurrent neural networks." Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/16531.
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Metaphors are an important literary figure that is
found in books or and daily use. Nowadays it is an essential task
for Natural Language Processing (NLP), but the dependence of
the context and the lack corpus in other languages make it a
bottleneck for some tasks such as translation or interpretation of
texts. We present a classification model using recurrent neural
networks for metaphor identification in Spanish sentences. We
tested our model and his variants on a new corpus in Spanish and
compared it with the current baseline using an English corpus.
Our best model reports an F-score of 52.5% for Spanish and
60.4% for English.Trabajo académico
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Chen, Jacob. "Embodied perception during walking using Deep Recurrent Neural Networks." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62171.
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Movements such as walking require knowledge of the environment in order to be robust. This knowledge can be gleaned via embodied perception. While information about the upcoming terrain such as compliance, friction, or slope may be difficult to directly estimate, using the walking motion itself allows for these properties to be implicitly observed over time from the stream of movement data. However, the relationship between a parameter such as ground compliance and the movement data may be complex and difficult to discover. In this thesis, we demonstrate the use of a Deep LSTM Network to estimate slope and ground compliance of terrain by observing a stream of sensory information that includes the character state and foot pressure information.Science, Faculty of
Computer Science, Department of
Graduate
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Jansson, Anton. "Predicting trajectories of golf balls using recurrent neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210552.
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This thesis is concerned with the problem of predicting the remaining part of the trajectory of a golf ball as it travels through the air where only the three-dimensional position of the ball is captured. The approach taken to solve this problem relied on recurrent neural networks in the form of the long short-term memory networks (LSTM). The motivation behind this choice was that this type of networks had led to state-of-the-art performance for similar problems such as predicting the trajectory of pedestrians. The results show that using LSTMs led to an average reduction of 36.6 % of the error in the predicted impact position of the ball, compared to previous methods based on numerical simulations of a physical model, when the model was evaluated on the same driving range that it was trained on. Evaluating the model on a different driving range than it was trained on leads to improvements in general, but not for all driving ranges, in particular when the ball was captured at a different frequency compared to the data that the model was trained on. This problem was solved to some extent by retraining the model with small amounts of data on the new driving range.Detta examensarbete har studerat problemet att förutspå den fullständiga bollbanan för en golfboll när den flyger i luften där endast den tredimensionella positionen av bollen observerades. Den typ av metod som användes för att lösa problemet använde sig av recurrent neural networks, i form av long short-term memory nätverk (LSTM). Motivationen bakom detta var att denna typ av nätverk hade lett till goda resultatet för liknande problem. Resultatet visar att använda sig av LSTM nätverk leder i genomsnitt till en 36.6 % förminskning av felet i den förutspådda nedslagsplatsen för bollen jämfört mot tidigare metoder som använder sig av numeriska simuleringar av en fysikalisk modell, om modellen användes på samma golfbana som den tränades på. Att använda en modell som var tränad på en annan golfbana leder till förbättringar i allmänhet, men inte om modellen användes på en golfbana där bollen fångades in med en annan frekvens. Detta problem löstes till en viss mån genom att träna om modellen med lite data från den nya golfbanan.
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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 the learning of the synaptic matrix on a local Hebbian mechanism. The third assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli, and being easily able to switch from one of these potential attractors to another in response to any incoming stimulus. Finally, since their introduction sixty years ago, cell assemblies have proved to be a powerful paradigm for brain information processing. After their introduction in artificial intelligence, cell assemblies became commonly used in computational neuroscience as a neural substrate for content addressable memories.
Based on these assumptions, this thesis provides a computer model of neural network simulation of a brain-like memory. It first shows experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the background, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause, but the consequence of the learning. However, it appears as an helpful consequence that widens the network’s encoding capacity. To learn the information to be stored, two supervised iterative Hebbian learning algorithm are proposed. One leaves the semantics of the attractors to be associated with the feeding data unprescribed, while the other defines it a priori. Both algorithms show good results, even though the first one is more robust and has a greater storing capacity. Using these promising results, a biologically plausible alternative to these algorithms is proposed using cell assemblies as substrate for information. Even though this is not new, the mechanisms underlying their formation are poorly understood and, so far, there are no biologically plausible algorithms that can explain how external stimuli can be online stored in cell assemblies. This thesis provide such a solution combining a fast Hebbian/anti-Hebbian learning of the network's recurrent connections for the creation of new cell assemblies, and a slower feedback signal which stabilizes the cell assemblies by learning the feed forward input connections. This last mechanism is inspired by the retroaxonal hypothesis.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Rodriguez, Paul Fabian. "Mathematical foundations of simple recurrent networks /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9935464.
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Otte, Sebastian [Verfasser]. "Recurrent Neural Networks for Sequential Pattern Recognition Applications / Sebastian Otte." München : Verlag Dr. Hut, 2017. http://d-nb.info/1149579382/34.
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Ahrneteg, Jakob, and Dean Kulenovic. "Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219.
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Background. This thesis focuses on the task of historical document semantic segmentation with recurrent neural networks. Document semantic segmentation involves the segmentation of a page into different meaningful regions and is an important prerequisite step of automated document analysis and digitisation with optical character recognition. At the time of writing, convolutional neural network based solutions are the state-of-the-art for analyzing document images while the use of recurrent neural networks in document semantic segmentation has not yet been studied. Considering the nature of a recurrent neural network and the recent success of recurrent neural networks in document image binarization, it should be possible to employ a recurrent neural network for document semantic segmentation and further achieve high performance results. Objectives. The main objective of this thesis is to investigate if recurrent neural networks are a viable alternative to convolutional neural networks in document semantic segmentation. By using a combination of a convolutional neural network and a recurrent neural network, another objective is also to determine if the performance of the combination can improve upon the existing case of only using the recurrent neural network. Methods. To investigate the impact of recurrent neural networks in document semantic segmentation, three different recurrent neural network architectures are implemented and trained while their performance are further evaluated with Intersection over Union. Afterwards their segmentation result are compared to a convolutional neural network. By performing pre-processing on training images and multi-class labeling, prediction images are ultimately produced by the employed models. Results. The results from the gathered performance data shows a 2.7% performance difference between the best recurrent neural network model and the convolutional neural network. Notably, it can be observed that this recurrent neural network model has a more consistent performance than the convolutional neural network but comparable performance results overall. For the other recurrent neural network architectures lower performance results are observed which is connected to the complexity of these models. Furthermore, by analyzing the performance results of a model using a combination of a convolutional neural network and a recurrent neural network, it can be noticed that the combination performs significantly better with a 4.9% performance increase compared to the case with only using the recurrent neural network. Conclusions. This thesis concludes that recurrent neural networks are likely a viable alternative to convolutional neural networks in document semantic segmentation but that further investigation is required. Furthermore, by combining a convolutional neural network with a recurrent neural network it is concluded that the performance of a recurrent neural network model is significantly increased.Bakgrund. Detta arbete handlar om semantisk segmentering av historiska dokument med recurrent neural network. Semantisk segmentering av dokument inbegriper att dela in ett dokument i olika regioner, något som är viktigt för att i efterhand kunna utföra automatisk dokument analys och digitalisering med optisk teckenläsning. Vidare är convolutional neural network det främsta alternativet för bearbetning av dokument bilder medan recurrent neural network aldrig har använts för semantisk segmentering av dokument. Detta är intressant eftersom om vi tar hänsyn till hur ett recurrent neural network fungerar och att recurrent neural network har uppnått mycket bra resultat inom binär bearbetning av dokument, borde det likväl vara möjligt att använda ett recurrent neural network för semantisk segmentering av dokument och även här uppnå bra resultat. Syfte. Syftet med arbetet är att undersöka om ett recurrent neural network kan uppnå ett likvärdigt resultat jämfört med ett convolutional neural network för semantisk segmentering av dokument. Vidare är syftet även att undersöka om en kombination av ett convolutional neural network och ett recurrent neural network kan ge ett bättre resultat än att bara endast använda ett recurrent neural network. Metod. För att kunna avgöra om ett recurrent neural network är ett lämpligt alternativ för semantisk segmentering av dokument utvärderas prestanda resultatet för tre olika modeller av recurrent neural network. Därefter jämförs dessa resultat med prestanda resultatet för ett convolutional neural network. Vidare utförs förbehandling av bilder och multi klassificering för att modellerna i slutändan ska kunna producera mätbara resultat av uppskattnings bilder. Resultat. Genom att utvärdera prestanda resultaten för modellerna kan vi i en jämförelse med den bästa modellen och ett convolutional neural network uppmäta en prestanda skillnad på 2.7%. Noterbart i det här fallet är att den bästa modellen uppvisar en jämnare fördelning av prestanda. För de två modellerna som uppvisade en lägre prestanda kan slutsatsen dras att deras utfall beror på en lägre modell komplexitet. Vidare vid en jämförelse av dessa två modeller, där den ena har en kombination av ett convolutional neural network och ett recurrent neural network medan den andra endast har ett recurrent neural network uppmäts en prestanda skillnad på 4.9%. Slutsatser. Resultatet antyder att ett recurrent neural network förmodligen är ett lämpligt alternativ till ett convolutional neural network för semantisk segmentering av dokument. Vidare dras slutsatsen att en kombination av de båda varianterna bidrar till ett bättre prestanda resultat.
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Galtier, Mathieu. "A mathematical approach to unsupervised learning in recurrent neural networks." Paris, ENMP, 2011. https://pastel.hal.science/pastel-00667368.
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Dans cette thèse nous tentons de donner un sens mathématique à la proposition : le néocortex se construit un modèle de son environnement. Nous considérons que le néocortex est un réseau de neurones spikants dont la connectivité est soumise à une lente évolution appelée apprentissage. Dans le cas où le nombre de neurones est proche de l'infini, nous proposons une nouvelle méthode de champ-moyen afin de trouver une équation décrivant l'évolution du taux de décharge de populations de neurones. Nous étudions donc la dynamique de ce système moyennisé avec apprentissage. Dans le régime où l'apprentissage est beaucoup plus lent que l'activité du réseau nous pouvons utiliser des outils de moyennisation temporelle pour les systèmes lents/rapides. Dans ce cadre mathématique nous montrons que la connectivité du réseau converge toujours vers une unique valeur d'équilibre que nous pouvons calculer explicitement. Cette connectivité regroupe l'ensemble des connaissances du réseau à propos de son environnement. Nous comparons cette connectivité à l'équilibre avec les stimuli du réseau. Considérant que l'environnement est solution d'un système dynamique quelconque, il est possible de montrer que le réseau encode la totalité de l'information nécessaire à la définition de ce système dynamique. En effet nous montrons que la partie symétrique de la connectivité correspond à la variété sur laquelle est définie le système dynamique de l'environnement, alors que la partie anti-symétrique de la connectivité correspond au champ de vecteur définissant le système dynamique de l'environnement. Dans ce contexte il devient clair que le réseau agit comme un prédicteur de son environnementIn this thesis, we propose to give a mathematical sense to the claim: the neocortex builds itself a model of its environment. We study the neocortex as a network of spiking neurons undergoing slow STDP learning. By considering that the number of neurons is close to infinity, we propose a new mean-field method to find the ''smoother'' equation describing the firing-rate of populations of these neurons. Then, we study the dynamics of this averaged system with learning. By assuming the modification of the synapses' strength is very slow compared the activity of the network, it is possible to use tools from temporal averaging theory. They lead to showing that the connectivity of the network always converges towards a single equilibrium point which can be computed explicitely. This connectivity gathers the knowledge of the network about the world. Finally, we analyze the equilibrium connectivity and compare it to the inputs. By seeing the inputs as the solution of a dynamical system, we are able to show that the connectivity embedded the entire information about this dynamical system. Indeed, we show that the symmetric part of the connectivity leads to finding the manifold over which the inputs dynamical system is defined, and that the anti-symmetric part of the connectivity corresponds to the vector field of the inputs dynamical system. In this context, the network acts as a predictor of the future events in its environment
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Nguyen, Thaovy Tuong. "Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103874.
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The Falling Creek Reservoir (FCR) in Roanoke is monitored for water quality and other key measurements to distribute clean and safe water to the community. Forecasting these measurements is critical for management of the FCR. However, current techniques are limited by inherent Gaussian linearity assumptions. Since the dynamics of the ecosystem may be non-linear, we propose neural network-based schemes for forecasting. We create the LatentGAN architecture by extending the recurrent neural network-based ProbCast and autoencoder forecasting architectures to produce multiple forecasts for a single time series. Suites of forecasts allow for calculation of confidence intervals for long-term prediction. This work analyzes and compares LatentGAN's accuracy for two case studies with state-of-the-art neural network forecasting methods. LatentGAN performs similarly with these methods and exhibits promising recursive results.Master of Science
The Falling Creek Reservoir (FCR) is monitored for water quality and other key measurements to ensure distribution of clean and safe water to the community. Forecasting these measurements is critical for management of the FCR and can serve as indicators of significant ecological events that can greatly reduce water quality. Current predictive techniques are limited due to inherent linear assumptions. Thus, this work introduces LatentGAN, a data-driven, generative, predictive neural network. For a particular sequence of data, LatentGAN is able to generate a suite of possible predictions at the next time step. This work compares LatentGAN's predictive capabilities with existing neural network predictive models. LatentGAN performs similarly with these methods and exhibits promising recursive results.
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Haddad, Josef, and Carl Piehl. "Unsupervised anomaly detection in time series with recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259655.
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Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these networks for unsupervised anomaly detection in time series are used in this study. We primarily use our own synthetic data sets in order to discover the networks’ robustness to noise and how they compare to each other regarding different characteristics in the time series. Our results shows that both networks can handle noisy time series and the difference in performance regarding noise robustness is not significant for the time series used in the study. LSTM outperforms HTM in detecting point anomalies on our synthetic time series with sine curve trend but a conclusion about the overall best performing network among these two remains inconclusive.Artificiella neurala nätverk (ANN) har tillämpats på många problem. Däremot försöker inte de flesta ANN-modeller efterlikna hjärnan i detalj. Ett exempel på ett ANN som är begränsat till att efterlikna hjärnan är Hierarchical Temporal Memory (HTM). Denna studie tillämpar HTM och Long Short-Term Memory (LSTM) på avvikelsedetektionsproblem i tidsserier för att undersöka vilka styrkor och svagheter de har för detta problem. Avvikelserna i denna studie är begränsade till punktavvikelser och tidsserierna är i endast en variabel. Redan existerande implementationer som utnyttjar dessa nätverk för oövervakad avvikelsedetektionsproblem i tidsserier används i denna studie. Vi använder främst våra egna syntetiska tidsserier för att undersöka hur nätverken hanterar brus och hur de hanterar olika egenskaper som en tidsserie kan ha. Våra resultat visar att båda nätverken kan hantera brus och prestationsskillnaden rörande brusrobusthet var inte tillräckligt stor för att urskilja modellerna. LSTM presterade bättre än HTM på att upptäcka punktavvikelser i våra syntetiska tidsserier som följer en sinuskurva men en slutsats angående vilket nätverk som presterar bäst överlag är fortfarande oavgjord.
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ANDERSSON, SANTIAGO GABRIEL, and MARTIN FAVRE. "Analysis and Evaluation of Recurrent Neural Networks in Autonomous Vehicles." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217336.
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Once upon a time cars were driven by the pure will and sweat of decent humans. Today technology has reached the point in which complex systems can drive the car with little or no human interaction at all. Whilst it does take away the sweet Sunday drive, one has to consider the positives. Over 90% of all vehicle accidents can be credited to the driver. City traffic can be optimised to avoid congestion. Additionally extending the morning nap to the car ride to work is truly something to strive for. One of the way autonomous driving can be achieved is through Artificial Neural Networks. These systems teaches a model how do drive a car through vast and vast amounts of data consisting of the state and the correct action. No manual logic required! One of the many issues these systems face is that the model only analyses the current state and has no inherent memory, just a million small independent decisions. This creates issues in situations like overtaking as it requires a longer plan to safely pass the other vehicle. This thesis investigates utilising the Recurrent Neural Networks which are designed to analyse sequences of states instead of a single one with hopes that this may alleviate the sequential hassles. This is done by modifying an 1/12 scale RC-car by mounting a camera in the front. The images were used to control both steering or velocity in three separate tests which simulates normal driving situations in which the sequence of events contain information. In all three scenarios three different networks were tested. One ordinary single-state model, a model evaluating 5 states and model evaluating 25. Additionally as a ground truth a human drove the same tests. These were qualitatively compared and evaluated. The test results showed that there indeed sometimes were an improvement in utilising recurrent neural networks but additional and more repeatable tests are required to define when and why.Traditionellt har bilar körts av antsändiga människor. Teknologin har idag dock kommit till den punkten då komplexa system kan köra med minimal eller full avsaknad av mänsklig interaktion. Medan det visserligen tar bort den trevliga söndagsturen så måste man tänka på fördelarna. I över 90% av alla fordonsolyckor är orsaken grundad i föraren. Stadstrafik kan bli optimerad för att undvika trafikstockningar. Dessutom att förlämga ens morgontur med hela bilresan till jobbet är verkligen något att sträva efter. Ett av sätten man kan uppnå autonom körning är genom artificiella neurala nätverk. Dessa system lär en modell hur man kör med hjälp av stora mängder data som består av ett tillstånd och dess korrekta handling. Minimal mängd manuell design krävd. En av de flera problem som Artificiella Neurala Nätverk har är att de inte har något minne, utan tar bara en stor mängd individuella beslut. Detta kan skapa problem i situationer som omkörning då det kräver en längre plan för att säkert ta sig runt andra bilen. Den här uppsatsen undersöker `Recurrent´ Neurala Nätverk som är designade för att analysera sekvenser av tillstånd iställer för ett enkelt tillstånd med hopp om att det kommer lindra de skventiella problemen. Detta är gjort genom att modifiera en 1/12 i skala radiostyrd bil med en kamera på framsidan. Dessa bilder används för att kontrollera både styrning eller hastighet i tre separata experiment som simulerar vanliga körningsscenarion i vilka sekvensen av tillstånd innehåller information. I alla tre experiment testades tre olika nätverk. Dessa analyserar respektibe 1, 5 och 25 tillstånd. Utöver dessa gjordes även experiment med en mänsklig förare som grundreferens. Resultaten jämfördes och evaluerades kvalitativt. Slutresultatet visade att det fanns tillfällen då det var bättre att analysera flera tillstånd, men att fler och mer repeterbara tester behövs för att kunna slå fast när och varför.
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Beneš, Karel. "Recurrent Neural Networks with Elastic Time Context in Language Modeling." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255481.
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Tato zpráva popisuje experimentální práci na statistické jazykovém modelování pomocí rekurentních neuronových sítí (RNN). Je zde předložen důkladný přehled dosud publikovaných prací, následovaný popisem algoritmů pro trénování příslušných modelů. Většina z popsaných technik byla implementována ve vlastním nástroji, založeném na knihovně Theano. Byla provedena rozsáhlá sada experimentů s modelem Jednoduché rekurentní sítě (SRN), která odhalila některé jejich dosud nepublikované vlastnosti. Při statické evaluaci modelu byly dosažené výsledky relativně cca. o 2.7 % horší, než nejlepší publikované výsledky. V případě dynamické evaluace však bylo dosaženo relativního zlepšení o 1 %. Dále bylo experimentováno i s modelem Strukturně omezené rekurentní sítě, ale ten se nepodařilo natrénovat k předpokládáným výkonům. Konečně bylo navrženo rozšíření SRN, pojmenované Náhodně prořidlá rekurentní neuronová síť. Experimentálně bylo potvrzeno, že RS-RNN dosahuje lepších výsledků v učení vlastního trénovacího korpusu a kombinace několika RS-RNN modelů přináší o 30 % větší zlepšení než kombinace stejného počtu SRN.
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Bolcato, Pietro. "Concurrent generation of melody and lyrics by recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284499.
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This work proposes a conditioned recurrent neural network architecture forconcurrent melody and lyrics generation. This is in contrast to methods thatfirst generate music and then lyrics, or vice versa. The system is trained to firstsample a pitch from a distribution, then sample a duration conditioned on thesampled pitch, and finally sample a syllable conditioned on the sampled pitchand duration. The evaluation metrics show the trained system generates musicand text sequences that exhibit some sensible musical and linguistic properties,and as further evaluation, it was applied in a human-AI collaboration for thegeneration of a song for the VPRO AI Song Contest. This highlighted thelimitations of the system: it can be a useful tool to augment the creative processof musicians, but it can not replace them. Finally, a shorter version of thisdissertation has been submitted as a paper for the ISMIR 2020 conference,and it is shown in appendix B.I detta arbete presenteras arkitektur för ett s.k. villkorat, återkommande (”conditioned,recurrent”) neuronnät for samtidig melodi och textgenerering. Denhär föreslagna modellen skiljer sig från tidigare modeller som som forst genererarmusik och sedan texter, eller vice versa. Systemet är tränat for att forstsampla en tonhojd fran en statistisk fordelning av tonhöjder i melodier, sedantesta duration utifrån den samplade tonhojden, och slutligen testa en stavelsebetingad av den samplade tonhojden och dess duration. Utvärderingen visar attdet tranade systemet genererar musik- och textsekvenser som uppvisar musikaliskaoch sprakliga egenskaper, och som en ytterligare utvardering anvandesdet i ett människa-maskin integrerat AI-samarbete for att generera en lat forVPRO AI Song Contest. Detta visade systemets begransningar och möjligheter,där systemet har potential att vara ett anvandbart verktyg for att stimuleramusikers kreativa process, men det kan inte ersatta dem. En kortare version avdenna uppsats (se bilaga B) har skickats in till ISMIR konferensen 2020.
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Daliparthi, Venkata Satya Sai Ajay. "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20651.
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Background: In Autonomous Driving Vehicles, the vehicle receives pixel-wise sensor data from RGB cameras, point-wise depth information from the cameras, and sensors data as input. The computer present inside the Autonomous Driving vehicle processes the input data and provides the desired output, such as steering angle, torque, and brake. To make an accurate decision by the vehicle, the computer inside the vehicle should be completely aware of its surroundings and understand each pixel in the driving scene. Semantic Segmentation is the task of assigning a class label (Such as Car, Road, Pedestrian, or Sky) to each pixel in the given image. So, a better performing Semantic Segmentation algorithm will contribute to the advancement of the Autonomous Driving field. Research Gap: Traditional methods, such as handcrafted features and feature extraction methods, were mainly used to solve Semantic Segmentation. Since the rise of deep learning, most of the works are using deep learning to dealing with Semantic Segmentation. The most commonly used neural network architecture to deal with Semantic Segmentation was the Convolutional Neural Network (CNN). Even though some works made use of Recurrent Neural Network (RNN), the effect of RNN in dealing with Semantic Segmentation was not yet thoroughly studied. Our study addresses this research gap. Idea: After going through the existing literature, we came up with the idea of “Using RNNs as an add-on module, to augment the skip-connections in Semantic Segmentation Networks through residual connections.” Objectives and Method: The main objective of our work is to improve the Semantic Segmentation network’s performance by using RNNs. The Experiment was chosen as a methodology to conduct our study. In our work, We proposed three novel architectures called UR-Net, UAR-Net, and DLR-Net by implementing our idea to the existing networks U-Net, Attention U-Net, and DeepLabV3+ respectively. Results and Findings: We empirically showed that our proposed architectures have shown improvement in efficiently segmenting the edges and boundaries. Through our study, we found that there is a trade-off between using RNNs and Inference time of the model. Suppose we use RNNs to improve the performance of Semantic Segmentation Networks. In that case, we need to trade off some extra seconds during the inference of the model. Conclusion: Our findings will not contribute to the Autonomous driving field, where we need better performance in real-time. But, our findings will contribute to the advancement of Bio-medical Image segmentation, where doctors can trade-off those extra seconds during inference for better performance.
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Ärlemalm, Filip. "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215088.
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The harbour porpoise is a toothed whale whose presence is threatened in Scandinavia. Onestep towards preserving the species in critical areas is to study and observe the harbourporpoise population growth or decline in these areas. Today this is done by using underwateraudio recorders, so called hydrophones, and manual analyzing tools. This report describes amethod that modernizes the process of harbour porpoise detection with machine learning. Thedetection method is based on data collected by the hydrophone AQUAclick 100. The data isprocessed and classified automatically with a stacked long short-term memory recurrent neuralnetwork designed specifically for this purpose.Vanlig tumlare är en tandval vars närvaro i Skandinavien är hotad. Ett steg mot att kunnabevara arten i utsatta områden är att studera och observera tumlarbeståndets tillväxt ellertillbakagång i dessa områden. Detta görs idag med hjälp av ljudinspelare för undervattensbruk,så kallade hydrofoner, samt manuella analysverktyg. Den här rapporten beskriver enmetod som moderniserar processen för detektering av vanlig tumlare genom maskininlärning.Detekteringen är baserad på insamlad data från hydrofonen AQUAclick 100. Bearbetning ochklassificering av data har automatiserats genom att använda ett staplat återkopplande neuraltnätverk med långt korttidsminne utarbetat specifikt för detta ändamål.