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 20 of 62 for “"learning rate"”.
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Time Aware Sigmoid Optimization : a new learning rate scheduling method
… give rise to faster training and a lower error rate, while bad choices could make the network not even converge, rendering the whole training process useless. Among all the existing hyperparameters, perhaps the one with the greatest importance is the learning rate, which controls how the weights …
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Learning rate optimisation of an image processing deep convolutional neural network
A dissertation submitted in fulfilment of the requirements for the degree of Master of Engineering Department of Electronics and Computer Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, 2021.
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An Experiment Concerning Simplified Spelling of Words Involving Learning Rate and Retention
Made available in DSpace on 2014-12-04T17:33:32Z (GMT). No. of bitstreams: 1 0023365.pdf: 8341434 bytes, checksum: a21d0d8e954f4e5eef761ac2a9131be6 (MD5) Previous issue date: 1957
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Rethinking methods to train deep neural networks : contributions of distinct regimes during training
… non-convex. Many of the methods used in deep learning which are informed by convex optimization work surprisingly well. The training dynamics of optimization methods such as momentum suggest that training occurs in distinct regimes, attributed to learning rate. In the low learning rate regime, …
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Effects of four different modality training programs on IQ and reading readiness performance in the lower socio-economic level kindergarten child
Four perceptual training programs were incorporated into an individualized kindergarten curriculum for disadvantaged children. One program stressed general auditory and visual perceptual skills, a second auditory skills specific to decoding, a third visual skills specific to decoding, and a fourth …
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The Effect of Spinal Cord Stimulation and Video Games Training on Body-machine Interface Control
… participants’ performance of motor control and learning rate through the above training framework. Participants' performance was recorded and quantified using four assessment metrics based on different center-out reaching tasks. Therefore, a multi-day experiment recruiting both unimpaired …
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The Impact of Threat on Behavioral and Neural Markers of Learning in Anxiety
… Decision science and in particular reinforcement learning models provide a quantitative framework to explain how the likelihood and value of such outcomes are estimated, thus allowing the measurement of parameters of decision-making that may differ between high- and low- anxiety groups. However, …
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Quantitative convergence analysis of dynamical processes in machine learning
… convergence of selected important machine learning processes, from a dynamical perspective, in order to understand and guide machine learning practices. Machine learning is becoming increasingly popular in various fields. Typical machine learning models consist of optimization and …
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Towards More Automated Statistical Inference And Machine Learning
… to collect keeps growing, the use of Machine Learning and statistical models has become more computationally demanding. Moreover, especially in industry applications, these models need to be trained quickly and efficiently, while also being updated frequently. With this increased complexity …
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Metagradient Descent: Differentiating Large-Scale Training
A major challenge in training large-scale machine learning models is configuring the training process to maximize model performance, i.e., finding the best training setup from a vast design space. In this work, we unlock a gradient-based approach to this problem. We first introduce an algorithm for …
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Eye tracking for cognition
… data provided brings a wealth of information on learning rate and clues to what the subject is thinking.
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Pole -mounted sonar vibration prediction using CMAC neural networks
… laboratory prototype, Analytical bounds of the learning rate of a CMAC neural network are derived which guarantee convergence of the weight vector in the mean. Both simulation and experimental results indicate the CMAC neural network is an effective tool for this vibration prediction problem.</p>
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Consistent high performance and flexible congestion control architecture
… continuously observes the connection between its rate control actions and empirically experienced performance, enabling it to use intelligent control algorithms to consistently adopt actions that result in high performance. We first build the above foundation of PCC architecture analytically prove …
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Classifying GPR images using convolutional neural networks
… of adjusted training options including initial learning rate, learn rate drop factor, and learn rate drop period; which had a positive impact on a part of the used models, while the option maximum number of epochs worked good with all of the used models. Results show that the first newly …
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A Transfer Learning Approach for Automatic Mapping of Retrogressive Thaw Slumps (RTSs) in the Western Canadian Arctic
… RTS mapping is being explored with deep learning methods. We employed a pre-trained Mask-RCNN model to automatically map RTSs on Banks Island and Victoria Island in the western Canadian Arctic, where there is extensive RTS activity. We tested the model with different settings, including …
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Association between Reward Sensitivity and Smoking Status in Major Depressive Disorder
… mechanisms by which nicotine influences the learning process is poorly understood. Here, we use a probabilistic learning task, functional magnetic resonance imaging and neurocomputational analyses, to show that chronic smoking is associated with higher reward sensitivity, along with lower …
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Generality of learning differences in mice genetically selected for differences in brain weight
… and its unselected control. Although on each learning test statistically reliable differences in learning rate were found within each selection, there was no consistent relationship (a) within selections and across tests or (b) within tests and across selections, between increased brain weight …
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Neurobiology of Learning and Valuation
… making a decision, evaluating outcomes, and learning from those outcomes to adjust future behavior is a central function of our nervous system. Determining the neural mechanisms of these cognitive processes is a crucial goal. One brain region, the posterior cingulate cortex (CGp), a central …
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Behavioral Training of Reward Learning Increases Reinforcement Learning Parameters and Decreases Depression Symptoms Across Repeated Sessions
Background: Disrupted reward learning has been suggested to contribute to the etiology and maintenance of depression. If deficits in reward learning are core to depression, we would expect that improving reward learning would decrease depression symptoms across time. Whereas previous studies have …
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Reinforcement learning algorithms to model learning and decision-making in individuals with depressive disorders
… reviews attempts to use reinforcement learning models to improve the way we conceptualise some of the processes happening in the brain in mental illness. The hope is that more clearly defining the problems we are dealing with will eventually have a positive impact on our ability to …
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