[CCoE Notice] Dissertation Announcement: Manojna Sistla, "Towards Fast and Robust Emerging Neural Networks"

[Greenwell, Stephen J]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Towards Fast and Robust Emerging Neural Networks
Manojna Sistla
November 18, 2024; 9 a.m. - 10:30 a.m. (CST)
Zoom: https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/3959794030__;!!LkSTlj0I!GNKzCjYN99hDmbLOWfJ0lCSmYBbm679_2FlbC9qPJb2Ol1qzUZKH-JQt2BQI-xauf7-lmhydqPY24wN8gUOcHoWWiMg$ <https://urldefense.com/v3/__https:/uh-edu-cougarnet.zoom.us/j/3959794030__;!!LkSTlj0I!A4kN_wAMjA2tKuIvwx50KSGcE4pISqNDY2l8ywCzNXz3pR-eXwB_2eiT_cU_jmmc94ExD4vQwFthsp3TcXISLERTsPaukZE$>
Committee Chair:
Xin Fu, Ph.D.
Committee Members:
Jinghong Chen, Ph.D. | Miao Pan, Ph.D. | Xuqing Wu, Ph.D. | Renjie Hu, Ph.D.
Abstract
Neural networks(NNs) are foundational to next-generation artificial intelligence, driving revolutionary advancements across diverse fields such as computer vision, healthcare, finance, and autonomous driving. However, the widespread adoption of these models introduces several significant challenges. Real-time applications, in particular, demand stringent adherence to execution delays and power consumption constraints, without compromising reliability. Furthermore, the increased deployment of neural networks has attracted malicious users, resulting in threats such as model disruption and theft of sensitive training data. This dissertation addresses these critical challenges within emerging neural network paradigms in computer vision, specifically focusing on quantum neural networks (QNNs), object detection models, and vision transformers.
NNs have become integral to computer vision, powering key applications like facial recognition, image classification, and autonomous driving. The drive for higher accuracy and efficiency has spurred a steady flow of new, advanced models, each pushing the boundaries of what's possible in these fields. Among the latest innovations are quantum neural networks (QNNs), which leverage rapid advancements in quantum computing-a new computational paradigm that uses principles of quantum mechanics to solve complex problems at remarkable speeds. These systems can handle tasks that traditional computers would find nearly impossible to complete within a practical timeframe. Early results with QNNs on current quantum hardware are promising, showing that they could potentially outperform conventional neural networks in both speed and computational power. Simultaneously, conventional neural network architectures have made substantial strides in performance. Convolutional neural networks (CNNs), especially the YOLO models, remain the preferred choice for object detection tasks due to their high performance and accuracy. Recently, transformer-based neural networks have gained traction for their ability to excel in both natural language and image processing through the self-attention mechanism, which captures global features of the images effectively, thereby producing superior results compared to the CNNs.
This dissertation investigates the performance limitations and security vulnerabilities inherent in these emerging neural network models. We propose methodologies to enhance their robustness, efficiency, and resilience to attacks, with particular emphasis on computer vision applications. Additionally, we outline potential directions for future research to further improve the reliability and effectiveness of neural networks in real-world applications.
[Engineered For What's Next]


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