AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The current study serves to identify the prerequisites for interpretable machine learning; however, transitioning to high-risk or safety-critical XAI domains will require future integration of higher-fidelity representations and domain-specific knowledge to meet rigorous real-world demands.
It is demonstrated that network architecture and its coeficients can be learned together by unifying concepts of evolutionary search within a population based traditional training process.
This work proposes an architecture that uses inter-class relations induced by the CNN that needs protection and has minimal resources overhead and does not require modifying the CNN, which makes it a competent solution that can be used with other error protection techniques.
This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today, and covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning
In this chapter, we investigate image-based malware family classification using an ensemble learning framework and a soft voting strategy. We consider malware binaries that have been converted into images using eight distinct conversion strategies. Three complementary feature extraction tracks are applied to these imag...
We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stores a new pair of key-value representations for every data point during training, and retrieves and recombines these representations through an attention mechanism at in...
This study systematically compares seven pre-trained feature extractors across three architectural families, convolutional neural networks (CNNs), Vision Transformers (ViTs), and self-supervised models to provide practical guidance on model selection for downstream deep learning tasks.
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