AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,893 papers
Code, dataset and trained weights for a stacked 2D-3D graph neural network that predicts all 22 consolidated GHS hazard classes simultaneously from molecular structure.
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, traditional GNNs often treat edges as static features, failing to capture the dynamic and contextual nature of relationships within a graph. This...
Abstract Flash calculations are fundamental for determining phase behavior under specified thermodynamic conditions. Conventional approaches rely on iterative procedures, including phase stability analysis and phase-split calculations, which are computationally demanding due to the nonlinear nature of the governing equ...
Automated theorem proving (ATP) is a cornerstone of artificial intelligence, aiming to mechanize mathematical reasoning. However, traditional ATP systems often struggle with scalability and efficiency, particularly for complex theorems. This paper proposes a novel approach utilizing Graph Neural Networks (GNNs) to addr...
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This paper introduces the Based on Cognitive Maps (BCNN) framework, a novel approach to neural network design that leverages cognitive maps for knowledge representation and reasoning. Traditional neural networks often struggle with representing complex relationships and performing logical inference, particularly in dom...
This paper addresses the limitations of existing Graph Neural Networks (GNNs) concerning the effective capture of underlying latent structural information within graphs. We propose a novel architecture incorporating a latent state representation learning module within the GNN framework. This module enables nodes to lea...
This paper explores a novel approach to graph-based data processing by drawing inspiration from biological neural networks. Traditional graph neural networks (GNNs) often rely on manually designed network architectures, which can be computationally expensive and may not optimally capture the inherent structure of graph...
This paper proposes a novel approach to knowledge graph construction by integrating neural symbolic reasoning engines. The core idea is to leverage the strengths of both neural networks (specifically Transformer-based models for reasoning) and traditional knowledge graph reasoning techniques. We introduce a dynamic kno...
Predictive maintenance (PM) aims to anticipate equipment failures and schedule maintenance proactively, minimizing downtime and operational costs. Traditional PM approaches often rely on static data and historical failure patterns. However, equipment systems are dynamic and evolve over time, influenced by operational c...
This paper presents a novel algorithm for neural network structure optimization based on graph theory. Traditional approaches often focus on static topology changes, while this method dynamically adjusts the graph's topology to adapt to data distribution. The core of the algorithm lies in representing the neural networ...
The performance of target-specific, ligand-based virtual screening models is strongly influenced by dataset characteristics, including data availability, class imbalance, and evaluation strategies. In this work, we perform a systematic evaluation of graph neural networks (GNNs) using a ChEMBL-derived dataset spanning...
This paper presents a novel approach to graph representation learning utilizing an adaptive neural network (ANN) for graph structure modeling. Traditional graph neural networks often suffer from static representations, failing to adapt to evolving graph structures. We propose a dynamically adjusting ANN architecture th...
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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