70 papers indexed here
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This paper presents a novel approach to graph neural network (GNN) learning, leveraging an adaptive graph neural network (AGNN) to dynamically learn the graph topology and subsequently utilize this topology for data modeling. Traditional GNNs typically rely on static graph structures, limiting their ability to effectiv...
Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a significant limitation of traditional GNNs is their inability to effectively handle evolving graph structures and temporal dependencies....
This paper introduces Geometric Topology Optimization Algorithms (GTOA) – a novel approach that integrates deep learning with topology optimization to accelerate complex structural simulations and design. We propose a novel "拓扑感知" algorithm utilizing Generative Adversarial Networks (GANs) to learn the geometric str...
This paper presents a novel approach to robot motion planning based on adaptive learning. Traditional robot motion planning methods often struggle to cope with dynamic and uncertain environments. This research leverages reinforcement learning (RL) to develop a system capable of autonomously learning optimal motion stra...
This paper explores a novel approach to reinforcement learning (RL) that integrates causal inference to enhance both the reliability and interpretability of decision-making processes. Traditional RL algorithms often operate as "black boxes," lacking transparency into the reasoning behind their actions. This research ad...
This paper introduces a Dynamic Semantic Network Generator (DSNG), a novel system designed to automatically construct and maintain semantic networks representing complex, dynamic systems. The core of the DSNG is a reinforcement learning (RL) framework that adapts the network's structure and connections in response to c...
This paper introduces Dynamic Context-Aware Neural Architecture Search (DCA-NAS), a novel approach to neural architecture search that addresses the limitations of traditional NAS methods by incorporating dynamic contextual information during the search process. The core claim of DCA-NAS is that real-time analysis of in...
This paper investigates the potential of dynamically self-organizing neural networks inspired by biological systems. Traditional static neural networks often struggle with adaptability and efficiency in complex environments. We propose a novel approach that leverages reinforcement learning to dynamically adjust connect...
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the complex system scheduling problem. Traditional scheduling methods often struggle to adapt to dynamic and intricate system environments, leading to suboptimal performance. This research proposes a novel framework leveraging MARL...
This paper explores the application of Deep Reinforcement Learning (DRL) to automate software testing. Traditional software testing methodologies heavily rely on predefined rules and templates, often proving inadequate against the complexity and variability of modern software systems. This research proposes a novel app...
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