AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
153 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Graph embeddings have emerged as a powerful technique for representing complex networks in a low-dimensional vector space. However, traditional graph embedding methods often lack interpretability, making it difficult to understand the underlying reasons for the generated embeddings. This paper proposes a novel dynamic...
This paper introduces a novel approach to enhancing Graph Neural Networks (GNNs) by incorporating topological information. The core idea is to explicitly model and learn the relationships between nodes and edges within a graph, leading to improved representation learning and training efficiency, particularly in complex...
This paper proposes a novel approach to neuro-symbolic programming that leverages a dynamic knowledge graph construction process. The core idea is to integrate neural networks with symbolic programming, enhancing the system's ability to learn and reason. A neural network is trained to generate symbolic rules and assert...
This paper explores the application of Graph Neural Networks (GNNs) to program structure optimization. Traditional program optimization techniques often rely on static analysis and hand-crafted rules, which can be limited in their ability to capture complex code dependencies and adapt to diverse program styles. We prop...
This paper presents a novel approach to intelligent perception systems by leveraging multi-scale information fusion. The core idea is to effectively integrate diverse data modalities – including low-resolution, high-resolution images, audio, and text – represented through a graph-based framework. This framework utilize...
This paper introduces the Dynamic Semantic Embedding Network (DSEN), a novel approach to program understanding and prediction that leverages the power of graph neural networks and continuous learning. The core claim of DSEN is to achieve dynamic comprehension and prediction of program behavior without explicit annotati...
Knowledge graph completion (KGC) is a critical task in leveraging the rich structural information embedded within knowledge graphs (KGs). Traditional graph neural networks (GNNs) often face challenges in effectively capturing the intricate relationships and dependencies within these graphs, particularly when dealing wi...
This paper proposes a novel approach to semantic understanding utilizing multi-scale graph neural networks (MS-GNNs). Traditional semantic understanding models often operate within a single scale of representation, limiting their ability to capture nuanced relationships and contextual information. Our method addresses...
This paper introduces a novel approach to knowledge graph reasoning based on Dynamic Graph Neural Networks (DGNNs). Traditional knowledge graph reasoning models often treat knowledge graphs as static structures, failing to capture the inherent dynamic evolution of entities and relationships. This work addresses this li...
This paper presents a novel approach to verifying complex temporal logic specifications on dynamic systems represented as graphs, addressing the significant computational challenges inherent in traditional verification methods. The core idea is to leverage the scalability of Probabilistic Neural Networks (PNNs) to appr...
Graph representation learning has achieved significant success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a critical limitation often exists when dealing with temporal data embedded within graphs – accurately capturing the evolving relationship...
The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.