82 papers indexed here
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This paper introduces a novel algorithm for graph neural network (GNN) graph structure adjustment, leveraging the principles of adaptive neural networks. Traditional approaches often struggle with fixed parameter settings, limiting the model's ability to effectively capture complex relationships within the data. Our pr...
This paper explores the application of Graph Neural Networks (GNNs) for automated software dependency discovery. Traditional methods for identifying dependencies rely heavily on manual annotation or rule-based approaches, often proving insufficient in complex software systems. We propose a novel approach that leverages...
This paper presents a novel approach to graph neural networks (GNNs) utilizing an adaptive neural network (ANN) to dynamically adjust graph structures. Traditional GNNs often rely on manually designed graph structures, limiting their applicability to complex data. We introduce a new algorithm that leverages an ANN to a...
This paper addresses the limitations of traditional knowledge graph reasoning, which primarily relies on correlational relationships, leading to potentially biased inferences. We propose a novel approach to multi-modal knowledge graph reasoning by explicitly modeling causal relationships between entities. Our core clai...
This paper presents a novel approach to social network analysis utilizing Explainable Graph Neural Networks (E-GNNs). Traditional graph neural networks, while effective in capturing complex relationships within networks, often operate as black boxes, hindering interpretability and trust. We propose a framework that int...
Quantum Machine Learning (QML) offers a paradigm shift in computational power, potentially enabling breakthroughs across diverse fields. This paper investigates the application of QML to reinforcement learning, specifically focusing on designing a novel framework that leverages dynamic quantum fields to enhance learnin...
This paper presents a novel approach to dynamic optimization and adaptive adjustment of quantum error correction (QEC) codes. Traditional QEC codes often rely on fixed parameters, which can be suboptimal in the presence of fluctuating noise environments. We introduce a framework that leverages the redundancy inherent i...
This paper explores the potential of algorithmic emergence to generate complex, fractal-like structures with inherent properties of self-replication and adaptation. We propose a novel design incorporating a genetic algorithm coupled with reinforcement learning to iteratively refine a structure, fostering self-organizat...
This paper investigates the application of probability-based constraint satisfaction (CS) algorithms to dynamic problem environments. Traditional CS methods often struggle with complex landscapes and require extensive search, leading to computational bottlenecks. We propose a novel reinforcement learning (RL) framework...
This paper investigates the challenges of achieving robust cooperative navigation in multi-agent reinforcement learning (MARL) scenarios, addressing the limitations of relying solely on extrinsic rewards. We propose a novel framework integrating intrinsic motivation – specifically, curiosity and surprise – into each ag...
This paper investigates the use of dynamic constraint satisfaction within fractal generation algorithms to enhance structure complexity and robustness. Traditional fractal algorithms often rely on fixed constraints, limiting the potential for intricate and diverse forms. We propose an algorithmic approach that utilizes...
This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significa...
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