Journal Publications (Algorithms Research / Machine Learning)¶
Parallel Quick Sort using Thread Pool Pattern¶
Abstract
Sorting algorithms, their implementations and their applications in modern computing necessitates improvements for sorting large data sets quickly and efficiently. This paper will analyze the performance of a multi-threaded quick sort implemented using the thread pool pattern. The analysis will be done by comparing the time required to sort various data sets and their memory constraints, against the native sorting implementations of the Dual Pivot Quicksort and Merge Sort using the Fork-Join framework in the Oracle Java 8 programming language. Analysis is done of the effect of different number of processor (cores) of the test machine, as well as the performance barrier due to the initial time taken to create “p” threads, p being the number of processors. This paper also analyzes the limitations of the inbuilt Java method Arrays.parallelSort() and how the proposed system overcomes this problem. Finally, it also discuss possible improvements to the proposed system to further improve its performance.
AdaSort: Adaptive Sorting using Machine Learning¶
Abstract
Sorting algorithms and their implementations in modern computing requires improvements in sorting large data sets effectively, both with respect to time and memory consumed. This paper is aimed at reviewing multiple adaptive sorting algorithms, on the basis of selection of an algorithm based on the characteristics of the data set. Machine Learning allows us to construct an adaptive algorithm based on the analysis of the experimental data. A review of algorithms designed using Systems of Algorithmic Algebra and Genetic Algorithms was performed. Both methods are designed to target different use cases. Systems of Algorithmic Algebra is a representation of pseudo code that can be converted to high level code using Integrated toolkit for Design and Synthesis of programs, while the Genetic Algorithm attempts to optimize its fitness function and generate the most successful algorithm.
Arxiv Pre-prints¶
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer¶
Abstract
Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.