AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,890 papers
Experimental results show the pipeline outperforms baselines in ranking, diversity, and explanations, proving that combining graph embeddings with agentic reasoning successfully bridges long-term profiles and immediate intent.
Generalisation capability of ML‐SGNN indicating that ML‐SGNN can be considered as a promising solution for credit classification with low sample size is considered.
This paper presents a physics-aware reduced-order method (ROM) for the efficient computation of wave-scattering problems. Standard model order reduction techniques, typically treating scattering as generic parameterized systems, frequently overlook the underlying physical structure, limiting their effectiveness in prac...
This work proposes a geometry-informed distributed acoustic scene understanding framework that leverages distributed microphones and uses an audio spectrogram transformer and a topology-aware graph neural network to fuse spatio-temporal acoustic features.
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The proposed MAGNN-DTA employs a cross-modal mutual attention module to explicitly capture bidirectional interactions between drug and protein modalities, enabling precise identification of key functional groups in drugs and critical binding residues in proteins.
This work presents a deep, global Structure-from-Motion framework based on learned view-graph aggregation that employs a permutation-equivariant, edge-conditioned graph neural network that takes noisy pairwise relative poses as input and outputs globally consistent camera extrinsics.
An adaptive large neighborhood search method guided by a heterogeneous graph neural network (HeteroGNN) method, termed HeteroGNN-ALNS, is developed to balance completion time, waiting time, and workload during the production process.
A circular permutation invariant graph neural network (CIRPIN) that identifies proteins related by a circular permutation across databases of solved and predicted structures and reveals previously unrecognized topological diversity within PDZ domains.
A theoretical framework for GNN transferability over sparse Random Geometric Graphs (RGGs) is established, capturing distance-dependent channel decay and spatial interference and extending this framework to conflict graph models, deriving equivalent scalability guarantees for link-level resource allocation policies.
GIN-CRC-Pareto, a graph-based, Pareto-optimized multi-task learning framework that simultaneously predicts miRNA-mRNA binding pairs, identifies seed match pairings, and classifies seed match subtypes, provides an effective and scalable approach for comprehensive identification of miRNA-target interactions in CRC.
A novel neural network called the Co-occurrence Graph Neural Network (CoGNN), which utilizes two co-occurrence graphs to establish user and item relationships and outperforms various baseline models in terms of recommendation accuracy and algorithm convergence.
A study of a Graph Neural Network-based model for track reconstruction in the context of MUonE experiment is presented, using simulated data corresponding to the test-run MUonE detector setup. The fully three-dimensional model successfully addresses both the reconstruction and particle identification challenges essenti...
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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