AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,893 papers
This work organizes the forward recursion of the multivariate Fa\`a di Bruno formula and its explicit backpropagation over a prescribed downward-closed set of multi-indices, with the Bell-polynomial convolutions tabulated once.
NEXUS is presented, a unified framework that establishes a systematic methodology for accelerating emerging AI workloads on commercial-off-the-shelf (COTS) Neural Processing Units (NPUs) and introduces a general systematic three-step approach.
This paper presents a novel approach to graph structure learning based on adaptive neural networks. Traditional graph neural networks often exhibit static structure, failing to effectively adapt to dynamic changes in the graph. We propose a method that utilizes an adaptive neural network to dynamically adjust the graph...
Abstract The power system is a core infrastructure for the national economy and energy security. As key assets in the grid, transmission-line components must be accurately inspected from UAV images despite small targets, large scale variation, complex backgrounds, and partial image degradation. This paper proposes KED-...
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Cybersecurity threats are increasingly complex and dynamic, posing significant challenges to traditional detection methods. This paper proposes the application of Graph Neural Networks (GNNs) to enhance cybersecurity threat detection by leveraging the inherent relational nature of network traffic data. We construct a g...
The design of effective Graph Neural Networks (GNNs) is a complex and often iterative process, frequently relying on expert intuition and extensive experimentation. This work introduces a novel approach leveraging meta-learning to automate the architecture design of GNNs. Our core claim is that a meta-learning model ca...
An explainable artificial intelligence framework for predictive jurisprudence that captures the temporal evolution of legal reasoning by jointly modeling semantic, structural, and causal relationships within judicial decisions is introduced.
Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and learning from graph-structured data, finding applications in diverse domains such as social network analysis, drug discovery, and recommendation systems. However, the training of GNNs presents unique privacy challenges due to the intricate r...
This paper explores the application of dynamic graph theory to machine learning, specifically focusing on the design and implementation of adaptive graph neural networks (AGNNs). Traditional neural network architectures often struggle with data that evolves over time, necessitating robust and adaptable learning strateg...
This paper introduces a novel graph neural network (GNN) architecture, dubbed Dynamic Topology Graph Neural Network (DTGNN), that leverages dynamic topology to enhance modeling capabilities. Traditional GNNs often struggle with non-linear data due to static graph representations. The DTGNN dynamically adjusts graph con...
This paper explores the application of Graph Neural Networks (GNNs) to symbolic logic reasoning. Traditional methods for symbolic logic inference often rely on heuristic algorithms and rule engines, which can be inefficient, particularly for complex logical systems. We propose a novel framework that leverages the struc...
This paper proposes a novel approach to probabilistic programming by leveraging the inherent capabilities of neuromorphic architectures. Traditional probabilistic programming often relies on manually constructed probability transition graphs, a process that can be both time-consuming and limiting in complex scenarios....
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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