Deep Transformer Neural Networks with Stochastic Competition
Date Issued
April 2024
Author(s)
Advisor
Abstract
Transformers have become one of the most successful architectures in deep learning,
experiencing a steady rise in popularity. These advanced networks have revolutionized
the field of Natural Language Processing (NLP) and are extending their influence into
new domains within artificial intelligence and beyond. The recent rise of large language
models, fundamentally reliant on Transformer architectures, highlights their effectiveness
and underscores their transformative impact.
This thesis delves into exploring further capabilities of this deep learning framework
by incorporating stochastic methodologies as an essential component of Transformer
networks. Our primary focus is on leveraging stochastic competition techniques, proven
to be highly advantageous in various contexts, as the cornerstone for developing highperforming
models. Instead of focusing on the extensively researched application areas
such as NLP, our research pivots to exploring two distinct and significantly different fields:
i) Sign Language Translation and ii) Tabular Data Modeling.
experiencing a steady rise in popularity. These advanced networks have revolutionized
the field of Natural Language Processing (NLP) and are extending their influence into
new domains within artificial intelligence and beyond. The recent rise of large language
models, fundamentally reliant on Transformer architectures, highlights their effectiveness
and underscores their transformative impact.
This thesis delves into exploring further capabilities of this deep learning framework
by incorporating stochastic methodologies as an essential component of Transformer
networks. Our primary focus is on leveraging stochastic competition techniques, proven
to be highly advantageous in various contexts, as the cornerstone for developing highperforming
models. Instead of focusing on the extensively researched application areas
such as NLP, our research pivots to exploring two distinct and significantly different fields:
i) Sign Language Translation and ii) Tabular Data Modeling.
Subjects
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PHD_Ανδρέας Βοσκού_2024.pdf
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