Sökning: "computational complexity"

Visar resultat 1 - 5 av 347 uppsatser innehållade orden computational complexity.

1. 1. Regulatory Driven Clustering of Single-Cell Data; Clustering of single-cell RNA sequencing from glioblastoma with a novel mathematical method

   Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

   Författare :Kári Kristjánsson; [2023-08-08]
   Nyckelord :;

   Sammanfattning : Cancer is a leading cause of death worldwide. Single-cell RNA sequencing has arisen as an important method to explore the gene expression of biological cells, including cancer cells. In this study, we deployed a computational algorithm known as ScRegClust to dissect single-cell RNA-sequencing (scRNA-seq) data from brain tumors. LÄS MER

3. 2. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis

   Magister-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Laura Galera Alfaro; [2023]
   Nyckelord :Explainable Artificial Intelligence; Learning To Rank; Local ModelAgnostic Interpretability; Ranking Generalized Additive Models;

   Sammanfattning : Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. LÄS MER

4. 3. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jiayi Feng; [2023]
   Nyckelord :DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

   Sammanfattning : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. LÄS MER

5. 4. Methods for Developing TinyConvolutional Neural Networksfor Deployment on EmbeddedSystems

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Egemen Yiğit Kömürcü; [2023]
   Nyckelord :;

   Sammanfattning : With the recent development in the Deep Learning area, computationally heavy tasks like object detection in images have become easier to compute and take less time to execute with powerful GPUs. Also, when employing sufficiently larger models, these daily tasks are predicted with greater accuracy. LÄS MER

7. 5. A Dual-Lens Approach to Loss Given Default Estimation: Traditional Methods and Variable Analysis

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :William Jaeckel; Nicolai Versteegh; [2023]
   Nyckelord :Loss given default; estimering; jämförande studie; variabelanalys; kreditförvaltning; utlåning till små och medelstora företag; riskanalys; Loss given default; estimering; jämförande studie; variabelanalys; kreditförvaltning; utlåning till små och medelstora företag; riskanalys;

   Sammanfattning : This report seeks to thoroughly examine different approaches to estimating Loss Given Default through a comparison of traditional estimation methods, as well as a deeper variable analysis on micro, small, and medium-sized companies using primarily regression decision trees. The comparative study concluded that estimating loss given default depends heavily on business-specific factors and data variety. LÄS MER