AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,904 papers
This paper introduces a novel dynamic topology optimization algorithm based on graph neural networks (GNNs). Traditional topology optimization methods often rely on manual design, struggling to adapt to complex systems exhibiting dynamic changes. Graph Neural Networks (GNNs) offer a powerful framework for automatically...
This paper proposes a novel network computation model termed "Space-Time Network Computation" (STNC). The core claim is that integrating spatial and temporal dimensions offers a more efficient approach to processing data exhibiting spatial and temporal dependencies. STNC leverages an extension of Graph Neural Networks...
This paper introduces a novel dynamic adaptive graph neural network (DAGNN) architecture designed to optimize network performance through automated structural adjustments. Traditional graph neural networks often rely on fixed, pre-defined structures, limiting their adaptability to diverse data. Our approach leverages a...
Predicting protein-protein interactions (PPIs) is a fundamental challenge in bioinformatics with significant implications for understanding biological systems and drug discovery. Traditional methods often struggle to capture the dynamic nature of PPIs, which are not static events but rather fluctuate over time due to v...
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Hundreds of millions of people interact with language models (LMs) every day, using them for tasks such as writing assistance and information seeking. As their range of applications grows, it becomes increasingly essential to consider the role of social context in how these systems are designed and deployed. In particu...
This paper presents a novel approach to complex system modeling leveraging the power of graph neural networks (GNNs). We construct a model designed to predict system behavior and states by effectively capturing relationships between nodes and edges within the system. The core mechanism of this approach is the utilizati...
This paper introduces a novel approach to neuro-symbolic reasoning utilizing cognitive graph structures. The core idea is to integrate the strengths of neural networks – their ability to learn complex patterns from data – with symbolic knowledge representations – providing explicit, interpretable reasoning. We propose...
This paper proposes a novel approach to analyzing time series data by combining Dynamic Time Warping (DTW) with Graph Neural Networks (GNNs). The core idea is to leverage DTW for effective feature extraction from time series, followed by a GNN to learn intricate relationships within the extracted representations. Tradi...
This paper proposes a novel approach to program optimization that leverages dynamic graph reasoning based on topology. Traditional static dependency graphs fail to adapt to runtime variations and resource constraints, leading to suboptimal performance. We introduce a system that utilizes a neural network to learn and r...
This paper investigates the representation of information flow within neural networks, proposing that the transmission of information is not a random process but rather governed by a specific topological structure. The core argument centers on the idea that the network's topology dictates the pathways through which inf...
Reliable data movement is essential to the Worldwide LHC Computing Grid. This project asks whether FTS queues that share a storage endpoint contain useful information about one another’s future state. I process 52,037,899 raw queue records from January 2026 into 23,820,642 sparse queue states sampled every 2 minutes. A...
Simulating quantum many-body ground states is constrained by the exponential scaling of the Hilbert space. Recent approaches rely on massive, parameter-heavy neural networks and resource-intensive Variational Monte Carlo (VMC) techniques. We compare two paradigms applied to the 2D Transverse Field Ising Model (TFIM) on...
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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