Multi-Dimensional Search Structures for Deep Learning Architectures
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P102Keywords:
Deep learning, Neural networks, Feature extraction, Hidden layers, Multi-layer perceptron, Machine learning, Artificial intelligence, Data processing, Model inference, PredictionAbstract
Deep learning has revolutionized various fields, from computer vision to natural language processing, by enabling the creation of highly complex and powerful models. However, the efficiency and scalability of these models are often constrained by the limitations of traditional search and indexing structures. This paper explores the application of multidimensional search structures in deep learning architectures, focusing on their potential to enhance model performance, reduce computational costs, and improve data retrieval efficiency. We delve into various multi-dimensional search structures, including k-d trees, ball trees, and locality-sensitive hashing (LSH), and discuss their integration into deep learning frameworks. We also present experimental results that demonstrate the effectiveness of these structures in different deep learning scenarios. Finally, we outline future research directions and potential applications of multi-dimensional search structures in the evolving landscape of deep learning
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