Hybrid Neural Networks - Integration and Applications: Investigating approaches for integrating multiple neural network architectures to leverage their complementary strengths
Keywords:
Hybrid Neural Networks, Integration, Neural Network ArchitecturesAbstract
Hybrid Neural Networks (HNNs) have emerged as a promising approach to combine the strengths of different neural network architectures. This paper explores various integration methods for HNNs and their applications across different domains. We first discuss the motivation behind using HNNs and then delve into the techniques used to integrate different architectures. We also highlight several successful applications of HNNs, including image classification, natural language processing, and reinforcement learning. Finally, we discuss the challenges and future directions of HNN research.
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Tatineni, Sumanth, and Anjali Rodwal. “Leveraging AI for Seamless Integration of DevOps and MLOps: Techniques for Automated Testing, Continuous Delivery, and Model Governance”. Journal of Machine Learning in Pharmaceutical Research, vol. 2, no. 2, Sept. 2022, pp. 9-41, https://pharmapub.org/index.php/jmlpr/article/view/17.
Prabhod, Kummaragunta Joel. "Advanced Machine Learning Techniques for Predictive Maintenance in Industrial IoT: Integrating Generative AI and Deep Learning for Real-Time Monitoring." Journal of AI-Assisted Scientific Discovery 1.1 (2021): 1-29.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
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