Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Recurrent Networks"”.
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Using Self-Organizing Maps for Computer Network Intrusion Detection
… in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a user's normal daily …
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Empirical Analysis of Neural Architectures and Side Information in Financial Time Series Forecasting
… the predictive capabilities of neural networks in financial time series forecasting, focusing on predicting the weekly close price of the SPY index. We explore the integration of options-derived features alongside traditional price data, compare recurrent architectures and …
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Recurrent neural networks in cognitive and vision neuroscience
… methodologies for biologically plausible neural networks, with a focus on models that incorporate recurrent dynamics characteristic of cortical circuits. First, we present an innovative approach for training stabilized supralinear networks, which are models of cortical circuits known to exhibit …
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Redes de neuronas recurrentes para el reconocimiento de patrones temporales
… Tesis es el estudio de las redes de neuronas recurrentes aplicadas al reconocimiento de patrones temporales. A través de una revisión de los conocimientos que existen actualmente sobre redes recurrentes, se llegarán a unas conclusiones fundamentales respecto a su capacidad y limitaciones …
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Spiking neural networks and their applications
<p>"Artificial neural networks (ANNs) have been developed as adaptable, robust function approximators for at least the last quarter-century. They have progressed through two generations, and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations …
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Improving Data-Driven Contact Localization and Force Estimation for Barometric Tactile Sensors
… 100 Hz. I construct both feed-forward and recurrent networks using this data, finding that a recurrent network achieves a 15% lower mean absolute error for angular contact localization on the sphere compared to prior methods. The recurrent architecture’s computational efficiency ensures …
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Constructing and Analyzing Neural Network Dynamics for Information Objectives and Working Memory
… processing capacity of neural circuits/networks. We contribute our efforts to devise a strategy to optimize the dynamics of the network at hand using information maximization as an objective function. In this vein, our principle contributions are in terms of mathematical formulation of …
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A neural-symbolic system for temporal reasoning with application to model verification and learning
… representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn …
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Systematic Development of Healthcare AI: From Data Curation, Algorithm Optimization, Benchmark Design and Clinical Applications
… and attention graph convolutional recurrent networks was developed to realize contactless, continuous and reliable radar-based vital signs monitoring in dynamic home environments. Through systematic data collection and algorithm optimization, the accurate heart rate can be obtained …
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On the induction of temporal structure by recurrent neural networks
… on its ability to model human language. Simple Recurrent Networks (SRNs) are a class of so-called artificial neural networks that have a long history in language modelling via learning to predict the next word in a sentence. However, SRNs have also been shown to suffer from catastrophic …
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Interpretable Deep Learning for Time Series
… in such fields are hesitant to use Deep Neural Networks (DNNs) that can be difficult to interpret. For example, in clinical research, one might ask, ``Why did you predict this person as more likely to develop Alzheimer's disease?". As a result, research efforts to improve the interpretability of …
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Subcortical Inputs Governing Cortical Network Activity
… coeruleus-noradrenergic system can switch local recurrent networks into different regimes via direct neuromodulation. Having characterized the nature of wakeful dynamics, I next sought to characterize how sensory information propagates through the cortex. The thalamocortical projection to layer 4 …
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Learning multiple solutions to computer vision problems
… [22, 23, 24, 25, 26] etc. Convolutional neural networks [27, 28] and/or Recurrent Neural Networks [29] trained to regress to a single value or classify to a single class label are the workhorse of most of these methods. However, many computer vision problems are ambiguous i.e. they have more …
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Computer vision based posture estimation and fall detection.
… study proposes Long Short-Term Memory (LSTM) recurrent networks-based fall detection. The proposed LSTM model uses the detected three region’s location as input features. LSTM is capable of using contextual information from the sequential input patterns. Therefore, the LSTM model was fed with …
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Data-centric methods for optimization and pattern discovery in networked systems
… data-driven solutions to problems in operational networks. The first problem is concerned with assessing the resilience of the US air transportation network from an operational perspective. As a complex network comprising over 5,000 public airports and countless interfaces with other …