This paper proposes a novel approach to knowledge discovery and reasoning, termed Cognitive Graph-Based Knowledge Discovery & Reasoning (CGKR). The core idea is to construct a dynamic cognitive graph and leverage its reasoning capabilities alongside those of traditional knowledge graphs. The system integrates the structural knowledge of knowledge graphs with the dynamic relationships inherent in cognitive graphs. Employing graph neural networks (GNNs) for node embedding and relation learning, combined with logical inference algorithms for knowledge reasoning, this framework facilitates adaptive learning and deeper knowledge extraction. We demonstrate the potential of CGKR to overcome limitations inherent in static knowledge graphs, particularly in scenarios involving evolving information and nuanced relationships. The presented methodology offers a significant advancement in the field of knowledge representation and reasoning, promising improved accuracy and adaptability in complex knowledge discovery tasks. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of non-Euclidean neural network architectures for enhanced generative modeling. Traditional neural networks, predicated on linear transformations and gradient descent, often struggle to capture the intricate patterns and high-dimensional space inherent in data. We propose a novel set of architectural designs that explicitly leverage concepts from manifold learning and graph theory to create networks with inherent diversity and adaptability. The core claim is that these architectures, by fostering emergent structures driven by geometry, can produce more expressive and robust generative models than standard approaches. The study focuses on developing novel network topologies and training strategies to encourage the formation of diverse manifolds and graph structures, ultimately leading to improved generative performance. This work addresses a significant gap in generative modeling by moving beyond simple linear transformations to explore more complex and flexible representations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel methodology for analyzing the temporal flow of information within neural networks, focusing on identifying bottlenecks and enhancing model stability. Traditional approaches often treat models as static entities, failing to capture the dynamic nature of information propagation. We introduce a causal graph representation of the network, meticulously tracking the flow of information across time. This allows for targeted optimization strategies, enabling us to identify and mitigate potential issues arising from temporal inconsistencies. The paper details the implementation of this method, demonstrating its effectiveness through a series of experimental studies. We highlight the benefits of this approach in improving model robustness and generalization capabilities.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, drug discovery, and recommendation systems. A fundamental limitation of many existing approaches is their reliance on fixed metrics to define node similarity and distance. These fixed metrics often fail to capture the nuanced relationships within complex graphs, leading to suboptimal embeddings. This paper proposes a novel framework for adaptive metric learning, which dynamically adjusts the embedding space based on the specific graph structure and the task at hand. The core idea is to train a metric learning model that learns to align nodes based on their contextual relationships, continuously refining the embedding space. We introduce a framework utilizing a contrastive loss function and a differentiable graph neural network (GNN) architecture to achieve adaptive graph embeddings. Experimental results demonstrate that our approach outperforms traditional graph embedding methods in several benchmark datasets, highlighting the effectiveness of adaptive metric learning for capturing complex graph structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to self-adaptive graph neural networks, aiming to automatically optimize the network structure based on data distribution. Traditional graph neural networks often require manual design of the network architecture, limiting their adaptability. Our proposed method employs an adaptive algorithm that dynamically adjusts node and edge weights during training, leading to enhanced model performance. We demonstrate the effectiveness of this approach through a series of experiments on various benchmark datasets. The core mechanism centers around a reinforcement learning framework to learn optimal graph structure.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores a novel algorithm for non-linear constraint satisfaction, focusing on adaptive variable weight optimization. Traditional approaches often struggle with variables that change over time, leading to suboptimal solutions. We introduce a dynamic variable weighting mechanism that automatically adjusts weights based on observed changes, enabling the algorithm to effectively handle evolving constraints. The core mechanism involves a reinforcement learning-inspired approach to dynamically adjust weights, optimizing the objective function while maintaining constraints. This approach significantly improves the performance of constraint-based optimization problems, particularly when dealing with dynamic and complex environments. The results demonstrate the effectiveness of our algorithm in a range of scenarios, showcasing its ability to adapt to changing conditions and achieve high-quality solutions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) relies heavily on reward signals to guide policy learning. However, conventional metrics like cumulative reward and average return frequently fail to capture the nuances of policy performance, particularly in complex environments. This work proposes a novel approach to validating RL policies based on information-theoretic principles. We argue that a policy's effectiveness is fundamentally tied to its ability to reduce uncertainty about the environment and maximize information gain. This paper introduces the use of metrics such as mutual information, entropy, and KL divergence to quantify the information content of state transitions and reward signals. These measures provide a more robust and interpretable assessment of policy performance compared to traditional reward-based evaluations. We demonstrate, through theoretical analysis and illustrative examples, how information-theoretic measures can effectively identify policy weaknesses, such as excessive exploration or suboptimal state selection, which are often missed by conventional metrics. The core contribution is a framework for evaluating RL policies that directly addresses the fundamental question of information flow, offering a potentially transformative shift in how we assess and compare RL algorithms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Multi-objective reinforcement learning (MORL) presents significant challenges due to the difficulty in balancing multiple, often conflicting, objectives. Traditional exploration methods often fail to adequately explore the complex multi-objective action space, leading to suboptimal solutions. This paper proposes a novel exploration strategy for MORL that leverages Pareto dominance to efficiently navigate this space. The core idea is to guide the agent's exploration towards regions where it can achieve better trade-offs between objectives, as defined by Pareto dominance. We formulate the exploration process using a dominance-based ranking of solutions, allowing the agent to systematically identify and target superior solutions. This approach aims to mitigate the "curse of dimensionality" inherent in MORL and improve the convergence towards optimal multi-objective policies. The theoretical framework outlines the key concepts and provides a basis for further research in this area.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel framework for dynamically mapping neuron topologies and generating adaptive neuron connections within neural networks. The core idea is to integrate topological network modeling with reinforcement learning to create a system capable of learning and adapting to complex neural network topologies. The system employs a multi-level modeling approach. The first level utilizes topological networks such as small-world networks and scale-free networks to represent the neural network. The second level leverages reinforcement learning algorithms, such as policy gradient or Actor-Critic, to train the connection strength between neurons. Crucially, connection strength is determined not only by neuron input/output activity but also by local topological information, including connection density and distance. A third level, an adaptive topology generation module, dynamically alters the topology structure based on network performance and learning objectives, adding or removing neurons and modifying their connections. This approach aims to optimize information processing and learning efficiency by mitigating information bottlenecks and accelerating signal propagation. The system demonstrates the potential to overcome limitations of existing neural network modeling methods and address complex learning and information processing challenges in neural networks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive GAN (GAN) training strategy designed to enhance model generation quality and stability. Traditional GAN training often relies on manual parameter adjustment, which can be time-consuming and suboptimal. Our approach leverages a dynamic adjustment mechanism to automatically tune the generator and discriminator parameters based on the training data. We propose a method for iteratively optimizing these parameters using a reinforcement learning framework, ensuring a smoother convergence and improved sample quality. The proposed strategy demonstrates significant improvements in generating diversity and fidelity compared to existing methods, offering a more efficient and robust training process.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to massive data stream processing based on adaptive control. The core idea is to dynamically adjust processing strategies based on the inherent characteristics of the data stream, thereby optimizing efficiency and resource utilization. We leverage reinforcement learning (RL) to learn stream features and utilize these learned features to dynamically adjust processing parameters. This adaptive mechanism allows the system to respond effectively to varying data stream patterns, a critical challenge in modern data processing environments. The proposed method aims to significantly improve throughput and reduce latency compared to traditional static processing methods. The theoretical framework and the core algorithm are detailed, showcasing the potential for enhanced performance in high-volume, high-velocity data streams. The presented approach offers a scalable solution for handling diverse data streams, making it suitable for applications such as network monitoring, financial analytics, and sensor data processing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum error correction (QEC) is a cornerstone of quantum information processing, enabling the reliable transmission of quantum information over noisy channels. However, the performance of QEC protocols is heavily reliant on the efficient allocation of quantum channels, a process that is often static and suboptimal. This paper introduces a novel framework for dynamic optimization of quantum channels within QEC protocols. The core idea leverages machine learning, specifically reinforcement learning, to model the characteristics of quantum channels and dynamically adjust channel assignment strategies. The proposed system learns from channel behavior, adapting to changes in noise levels, load, and other relevant factors. This adaptive approach significantly enhances the robustness and efficiency of quantum communication. The theoretical analysis demonstrates a substantial improvement in error correction performance compared to traditional, static channel allocation schemes. The system's ability to respond in real-time to channel conditions represents a critical step toward practical quantum communication networks. Key performance metrics, such as the quantum error rate (QER) and the overhead associated with channel allocation, are rigorously evaluated under diverse scenarios.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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