57 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.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems often struggle with privacy concerns and convergence issues, particularly when dealing with heterogeneous data distributions across clients. This pa...
Abstract: Develop a new method for efficiently modeling stochastic diffusion processes within deep neural networks, improving the stability and generalization of these models. Core Claim: Develop a new method for efficiently modeling stochastic diffusion processes within deep neural networks, improving the stability an...
This paper presents a novel approach to graph structure learning based on adaptive neural networks. Traditional graph neural networks often exhibit static structure, failing to effectively adapt to dynamic changes in the graph. We propose a method that utilizes an adaptive neural network to dynamically adjust the graph...
Cybersecurity threats are increasingly complex and dynamic, posing significant challenges to traditional detection methods. This paper proposes the application of Graph Neural Networks (GNNs) to enhance cybersecurity threat detection by leveraging the inherent relational nature of network traffic data. We construct a g...
The design of effective Graph Neural Networks (GNNs) is a complex and often iterative process, frequently relying on expert intuition and extensive experimentation. This work introduces a novel approach leveraging meta-learning to automate the architecture design of GNNs. Our core claim is that a meta-learning model ca...
Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and learning from graph-structured data, finding applications in diverse domains such as social network analysis, drug discovery, and recommendation systems. However, the training of GNNs presents unique privacy challenges due to the intricate r...
This paper explores the application of dynamic graph theory to machine learning, specifically focusing on the design and implementation of adaptive graph neural networks (AGNNs). Traditional neural network architectures often struggle with data that evolves over time, necessitating robust and adaptable learning strateg...
This paper introduces a novel graph neural network (GNN) architecture, dubbed Dynamic Topology Graph Neural Network (DTGNN), that leverages dynamic topology to enhance modeling capabilities. Traditional GNNs often struggle with non-linear data due to static graph representations. The DTGNN dynamically adjusts graph con...
This paper explores the application of Graph Neural Networks (GNNs) to symbolic logic reasoning. Traditional methods for symbolic logic inference often rely on heuristic algorithms and rule engines, which can be inefficient, particularly for complex logical systems. We propose a novel framework that leverages the struc...
This paper proposes a novel approach to probabilistic programming by leveraging the inherent capabilities of neuromorphic architectures. Traditional probabilistic programming often relies on manually constructed probability transition graphs, a process that can be both time-consuming and limiting in complex scenarios....
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...
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...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
