ABSTRACT: This paper presents a theoretical framework for parallelizing the FD3 algorithm, which estimates the capacity, information, and correlation dimensions of chaotic time series using the ...
This paper presents a theoretical framework for parallelizing the FD3 algorithm, which estimates the capacity, information, and correlation dimensions of chaotic time series using the box-counting ...
This repository contains the assignments and projects completed during my High Performance Computing (HPC) course at University of Thessaly. The coursework focuses on utilizing advanced computing ...
Abstract: Predictive marketing is vital for developing effective marketing strategies by utilizing consumer data to anticipate behaviors and optimize decision-making. This research employs k-means ...
Abstract: Text parallelization is a crucial aspect of natural language processing, aiming to enhance the efficiency of information retrieval and analysis. This project focuses on leveraging the Term ...
UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique that can be used for visualization, feature extraction, and preprocessing of high-dimensional data. Unlike ...
With the growing model size of deep neural networks (DNN), deep learning training is increasingly relying on handcrafted search spaces to find efficient parallelization execution plans. However, our ...
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