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 111 for “"Model Complexity"”.
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Model complexity and risk aversion in decision analysis
Models are often formulated to aid in decision-making. However, the details included or excluded are often determined with minimal examination of the effects. There is a tendency to make models more complex than merited. Yet, decision makers’ risk preferences are often ignored without considering …
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Deciding among models : a decision-theoretic view of model complexity
… the trade-off between the cost of adding complexity to a model and the value added to the results within the context of decision-making. It seeks to determine how complex a model should be in order to fit it to the purpose at hand. The report begins with a discussion on general modeling …
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The control and transformation metric: a basis for measuring model complexity
The purpose of this report is to develop a complexity metric suitable for discrete event simulation model representations. Current software metrics, based upon graphical analysis or static program characteristics, do not capture the influence on complexity stemming from the inherent dynamics of a …
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"MODEL COMPLEXITY AND VARIABLE SELECTION IN MAXENT NICHE MODELS: ANALYSES FOR RODENTS IN MADAGASCAR"
<p>Ecological niche models (ENMs) characterize the relationship between localities where a species is known to occur and the abiotic characteristics of these regions. While widely used, ENMs remain subject to several outstanding issues, including those related to model complexity and violation of …
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A Comprehensive Analysis of Deep Learning for Interference Suppression, Sample and Model Complexity in Wireless Systems
… and suppression, and it thoroughly examines complexity (sample and model) issues that arise from using deep learning. First, we address the knowledge gap in the literature with respect to the state-of-the-art in deep learning-based interference suppression. To account for the limitations of …
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Machine learning of image analysis with convolutional networks and topological constraints
… demonstrate that a learning approach with high model complexity, but zero prior knowledge about any specific image domain, can outperform existing techniques even in the challenging area of natural image processing. We also present results that establish how convolutional networks are closely …
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Appley: Approximate Shapley Values for Model Explainability in Linear Time
<p>We have seen complex deep learning models outperforming human benchmarks in many areas (e.g. computer vision, natural language processing). Clever architectures and higher model complexity are two of the major drivers of such outstanding performances. Higher model complexity generally makes the …
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A model generalization study in localizing indoor cows with cow localization (colo) dataset
… task difficult, such as the scarcity of data for model fine-tuning and the inability to generalize models effectively. To address these challenges, we introduces COLO (COw LOcalization), a publicly available dataset comprising localization data for Jersey and Holstein cows under various lighting …
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Robust Speaker Diarization for Single Channel Recorded Meetings
… detection method, which adjusts the non-speech model complexity according to the noise length ratio. Second, a new speaker change point detection measure was derived based on the Fisher Linear Discriminate Analysis to help detect short speaker turns. Third, the Equal Weight Penalty Criterion was …
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Texture-based Deep Neural Network for Histopathology Cancer Whole Slide Image (WSI) Classification
… by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological images compared to normal regions. The proposed method outperformed the current …
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Bridge Pier Surface Defect Detection Based on Improved YOLOV9
… mean Average Precision (mAP50) over the baseline model. Furthermore, this is accomplished with a reduction in model complexity, as evidenced by a 9.8% decrease in the number of parameters and a substantial reduction in computational demand, quantified as a 7.5 GFLOPS decrease. This study not only …
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Learning Morphology for Open-Vocabulary Neural Machine Translation
… during training. In addition to controlling the model complexity, this limitation is also related to the difficulty of learning accurate word representations under conditions of high data sparsity. This problem is an important bottleneck on performance, especially in morphologically-rich …
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ASSET PRICING OPTIMIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS
… the often-held belief that more complex, deeper models are invariably superior. Instead, we found that a simpler, less computationally intensive model can provide comparable results, and potentially do so with greater efficiency. While deep learning certainly has its merits, especially for more …
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Tailoring Complexity of Model-Based Controllers for Legged Robots
… and often conflicting control objectives. While model-based controllers can address these challenges using online optimization, they have high computational demands. Model predictive control (MPC) provides closed-loop stability with online trajectory optimization, but achieving real-time rates is …
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Statistical Recursive Estimation Algorithms for Speaker Adaption
… developed and applied to direct hidden Markov model parameter estimation. Then an online Bayesian learning technique is proposed for recursive maximum a posteriori estimation of tree-structured linear regression and affine transformation parameters. This technique has the advantages of …
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Understanding and Estimating the Adaptability of Domain-Invariant Representations
… from the training distribution, machine learning models can perform poorly and wrongly overestimate their performance. In this work, we aim to better estimate the model’s performance under distribution shift, without supervision. To do so, we use a set of domain-invariant predictors as a proxy for …
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Artificial Neural Network-Based Flood Forecasting: Input Variable Selection and Peak Flow Prediction Accuracy
… natural disaster in Canada. Flow forecasting models can be used to provide an advance warning of flood risk and mitigate flood damage. Data-driven models have proven to be suitable for flow forecasting applications, yet there are several outstanding challenges associated with model …
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Axisymmetric theory and the interactive, asymmetric monsoon
… monsoon is studied in a general circulation model with idealized representations of continental geometry and simple physics. The axisymmetric theory is expanded to explain the location of the monsoon; assuming quasi-equilibrium, the poleward boundary of the monsoon circulation will be …
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Incorporation of Hysteretic Effects in Model-Order Reduction Analysis of Magnetic Devices
… development and simulation of wide-bandwidth models requires detailed, physics-based simulations that utilize significant computational resources. Balancing the trade-offs between model computational overhead and accuracy can be cumbersome, especially when the nonlinear effects of saturation …
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Two Papers of Financial Engineering Relating to the Risk of the 2007--2008 Financial Crisis
… we construct the Spatial Capital Asset Pricing Model and the Spatial Arbitrage Pricing Theory to characterize the risk premiums of futures contracts on real estate assets. We also provide rigorous econometric analysis of the new models. Empirical study shows there exists significant spatial …
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