John Tsiligaridis, Department of Math and Computer Science, Heritage University, Toppenish, WA, USA
Autoencoders (AEs) are Deep Learning (DL) models that are well known for their ability to compress and reconstruct data. When an AE compresses input data, a latent space is created which yields a compressed representation of the original data with a smaller set of features. Genetic Algorithms (GAs) based on evolutionary principles can be used to optimize various hyperparameters of a DL model. This work involves two tasks. First, it focuses on the application of an AE on image data along with various configurations of the AE structure and its constituent encoder/decoder structure using Multi-Layer Perceptrons (MLPs). Visualizations of the AE loss functions during training are provided, along with various latent space results obtained using clustering techniques. The second focus of the paper is on the application of the GA on a Convolutional AE where optimization of the Convolutional Neural Networks (CNN) encoder/decoder structures is done by converting the architecture into genes for image classification. We see that the AE is a flexible and robust model that can successfully be applied on a variety of image datasets and the GA model surpasses the AE model.
Machine Learning, Deep Learning, Autoencoders, Genetic Algorithms.
Sai Kiran Padmam, Partha Sarthi Samal,independent,United States of America
Online retail has come a long way since its early days of static web pages and manual price comparisons. The new frontier embraces artificial intelligence (AI) to interpret user queries, mediate real-time auctions among multiple vendors, and deliver personalized recommendations at blazing speeds. This paper highlights how such AI agents operate under the hood, drawing upon machine learning, reinforcement learning, and multi-agent coordination principles. We also offer glimpses into emerging research challenges and future directions that may reshape online shopping entirely.
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