159 papers indexed here
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This paper introduces a novel graph-based optimization algorithm specifically designed to address non-linear geometric constraints. Traditional optimization methods often struggle with complex geometric designs due to the inherent limitations of their geometric representation. This work proposes a framework leveraging...
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 introduces a novel dynamic graph neural network (GNN) architecture leveraging adaptive deep learning techniques to dynamically adjust graph connectivity based on input data. Traditional GNN models often require manual parameter tuning, limiting their adaptability to dynamic datasets. Our proposed approach em...
This paper explores the application of Graph Neural Networks (GNNs) for automated drug molecule design. Traditional drug discovery relies heavily on empirical methods and serendipitous findings, often suffering from high costs and low success rates. We propose a novel approach that leverages the inherent structural inf...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems, by effectively learning patterns from graph-structured data. However, a significant limitation of GNNs is their inherent opaqueness—the "black box" nature...
This paper investigates the application of Graph Neural Networks (GNNs) for the automated identification and analysis of complex network topologies. Traditional network analysis methods often struggle to capture the intricate relationships and emergent patterns found in complex networks, such as social networks, biolog...
This paper explores the application of Graph Neural Networks (GNNs) for causal discovery directly from observational data. Traditional causal inference methods often rely on strong assumptions regarding underlying causal mechanisms, which can be difficult to validate. We propose a novel framework where a GNN learns cau...
This paper proposes a novel approach to modeling human memory by representing it as a dynamic cognitive graph. The core idea is to map the complex neural activation patterns observed during memory processes onto a graph structure where nodes represent concepts and edges represent the strength and temporal relationships...
Adaptive Self-Design Algorithm is a novel algorithmic framework designed to automate the design and optimization of mathematical formulas, particularly within complex non-standard and nonlinear equation systems. This approach leverages a probabilistic search algorithm, incorporating reinforcement learning and genetic a...
This paper presents a novel approach to symbolic artificial intelligence (AI) that addresses the limitations of traditional, manually-engineered rule-based systems. The core challenge in symbolic AI has historically been the extensive and often laborious process of crafting and maintaining a comprehensive rule set. Exi...
This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Po...
This paper introduces the Adaptive Spatiotemporal Neural Models (ASPNM), a novel approach to time series prediction that addresses the limitations of traditional, static neural network models. The core idea is to design a neural network architecture capable of dynamically adjusting its internal parameters and behaviora...
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