AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,934 papers
This paper addresses the limitations of current deep learning models in performing abstract symbolic reasoning. The core argument is that deep learning's reliance on pattern recognition hinders its ability to grasp underlying concepts and relationships, leading to a lack of genuine understanding. We propose a novel hie...
This paper introduces a novel neuro-symbolic reasoning framework centered around the dynamic construction of a knowledge graph. The core challenge in integrating neural networks and symbolic reasoning lies in the inherent differences in their representations and the difficulty in seamless translation between them. Our...
This paper proposes a novel graph neural network (GNN) optimization algorithm based on a dynamically adjusted attention mechanism. Traditional GNN optimization methods often rely on fixed architectures, limiting their adaptability to data variations. Our proposed algorithm leverages a self-adaptive strategy to dynamica...
The goal of this paper is to develop a novel approach to automated generation using self-adaptive graph neural networks (SA-GNNs). Traditional graph neural networks (GNNs) often struggle with adapting to dynamic data, limiting their effectiveness in complex generative tasks. This work introduces a new model, termed SA-...
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This paper introduces a novel architecture for Graph Neural Networks (GNNs) designed to effectively handle multi-scale spatio-temporal data. The core concept revolves around decomposing time-series data into multiple scales, each represented by a corresponding graph structure. A GNN is then employed to learn dependenci...
Graph embeddings are a fundamental tool for representing graph data, offering insights into network structure and relationships. Traditional methods often employ fixed weights, limiting the flexibility and scalability of the representation. This paper introduces a novel non-local graph embedding method that dynamically...
This paper introduces a novel approach to Neural Architecture Search (NAS) that leverages dynamic topology optimization based on dependency relationships. Traditional NAS methods often suffer from inefficiency and limited generalization due to the use of static search spaces and a lack of consideration for intricate la...
This paper presents a novel neural network structure optimization method leveraging adaptive graph neural networks (GNNs). The core challenge in training GNNs lies in the dynamic adjustment of connection weights, which often requires extensive hyperparameter tuning. We introduce a new framework that dynamically adjusts...
Graph neural networks (GNNs) have emerged as a powerful tool for various machine learning tasks involving graph-structured data. However, traditional GNN architectures often struggle with noisy and uncertain graph environments, limiting their performance. This paper proposes a novel self-adaptive graph neural network t...
This dataset contains the data files used to train the Graph Neural Network (GNN) model. Tetrahedral.pt — data corresponding to the quarter-filled triangular lattice. Trigonal.pt — data corresponding to the half-filled triangular lattice. Neel.pt — data corresponding to the half-filled square lattice. The files are sto...
The solvent-accessible surface area (SASA) of amino acid residues is a crucial parameter for protein structure analysis; however, precise computational methods such as FreeSASA are computationally expensive. As an alternative, empirical approximations based on residue interaction network (RIN) graphs can offer high spe...
benchmarks demonstrate that GRACE-OFF achieves higher MD performance than comparable MACE-OFF models in both single and double precision, establishing GRACE-OFF as an accurate and computationally efficient foundation potential for routine simulations of organic liquids and biomolecular systems.
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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