159 papers indexed here
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Autonomous robot navigation necessitates robust perception and decision-making capabilities that can adapt to dynamic and unpredictable environments. Traditional graph neural networks (GNNs) often rely on static graph structures, failing to effectively represent and reason about the constantly evolving relationships wi...
This paper investigates the application of Graph Neural Networks (GNNs) to the domain of hardware design space exploration. Traditional design exploration methods often struggle to effectively capture and optimize the intricate dependencies inherent in complex hardware architectures. We propose a novel framework utiliz...
This paper introduces a novel neural network architecture specifically designed to effectively handle non-Euclidean data distributions. Traditional deep learning models often struggle with data that doesn't conform to Euclidean space, requiring extensive transformations or specialized data representations. Our proposed...
This paper proposes a novel approach to time series analysis utilizing graph-based predictive coding. Traditional methods often struggle with capturing complex temporal dependencies and lack inherent interpretability. Our framework constructs a graph representation of the time series data, where nodes represent individ...
The increasing prevalence of complex AI models, particularly deep neural networks, in critical decision-making domains has raised significant concerns regarding transparency and trust. These "black-box" models often operate as opaque systems, making it challenging to understand *why* a particular decision was made. Thi...
This paper introduces a novel learning algorithm, Temporal Relational Graph Learning (TRGL), designed to effectively capture and leverage relational structure within dynamic graphs, explicitly incorporating temporal dependencies. TRGL addresses the limitations of existing approaches by employing a "relational memory" m...
This paper explores the development of an algorithm designed to perform conceptual abstraction and reasoning within cognitive architectures, mirroring human cognitive processes. The core of this work lies in a combined approach utilizing knowledge graphs, neural networks, and Bayesian networks. The system aims to captu...
This paper presents the Temporal Recurrence Network (TRN), a novel data compression technique that leverages dynamic network adjustments based on temporal patterns within datasets. Traditional compression algorithms often rely on static compression methods, failing to effectively exploit the inherent sequential structu...
This paper explores a novel approach to space geometry optimization using graph-based techniques. Traditional methods often struggle with complex scenarios, necessitating dynamic adjustments. We introduce a framework centered around graph traversal and simplification to dynamically optimize spatial geometry, including...
Social network influence propagation is a complex phenomenon significantly impacted by the dynamic nature of user interactions and evolving network structures. Traditional static graph embedding methods, which primarily focus on capturing the structural information of a network at a single point in time, often fall sho...
This paper introduces a novel dynamic balancing algorithm based on graph neural networks (GNNs) that automatically learns and adjusts complex system dynamics, offering predictable and adaptive balancing strategies. The core mechanism involves constructing a state representation using GNNs and employing self-attention m...
This paper presents a novel framework for analyzing the complexity of graph neural networks (GNNs) utilizing adaptive graph neural networks (GNNs). The core aim is to automatically identify and quantify the complexity of a GNN, providing actionable complexity control strategies. We propose a dynamic complexity adjustme...
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