This paper introduces Dynamic Topology Dependency Learning (TDTL), a novel approach to neural architecture search (NAS) that leverages the inherent dependencies within network topology to achieve more efficient architecture optimization. The core idea is to train a "topology controller" using reinforcement learning (RL) to dynamically suggest modifications to the network's connections – additions, deletions, or alterations – based on the network's current performance and structure. Unlike traditional NAS methods that rely on predefined search spaces or heuristics, TDTL learns directly from the network itself, enabling an adaptive and exploratory architecture optimization process. The learned topology dependencies are hypothesized to capture relationships between topological structure, task complexity, and data distribution characteristics. This approach presents a significant departure from existing NAS paradigms and holds the potential to unlock more effective and automated architecture discovery. The primary objective is to define a framework that learns the dependencies between neural network topology and its performance, ultimately leading to architectures optimized for specific tasks. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 Policy Optimization (PPO), we can significantly improve network efficiency and generalization capabilities. NTAD employs a "Neuro-Topology Evaluator" – a neural network itself – to continuously assess the performance of the current connection graph, considering metrics such as accuracy, latency, and energy consumption. The PPO algorithm then adjusts connection weights and introduces or removes neurons to maximize the evaluator's score. Unlike traditional training methods relying on static graphs and fixed optimization targets, NTAD offers a dynamic, self-optimizing solution capable of handling non-static, high-dimensional input data and autonomously discovering optimal neural connection patterns. The methodology presented here represents a significant advancement in neural network design, shifting the focus from solely adjusting weights to actively shaping the network's physical structure.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores a novel approach to understanding and manipulating universal chaos within complex systems, moving beyond static parameter settings. We propose a method based on Bayesian inference and reinforcement learning to dynamically optimize parameters of a chaotic system, fostering stable, high-dimensional states. The core mechanism involves iteratively assessing instability, adjusting parameters using reinforcement learning to minimize the fluctuation of the system's state, and employing Bayesian inference to ensure consistent and robust parameter selection. This work addresses a critical limitation in current chaos theory – the reliance on fixed settings – and offers a framework for automated stabilization and exploration of complex dynamical systems. The paper details the theoretical foundation, implementation, and preliminary results demonstrating the effectiveness of this adaptive approach in maintaining stable states across a range of parameter settings.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to dynamic symbolic computation by integrating temporal logic reasoning with reinforcement learning. The core idea is to enable a system to adaptively learn and optimize its symbolic representations through a feedback mechanism. We introduce an agent that utilizes temporal logic constraints to guide its manipulation of symbolic representations, and reinforcement learning to optimize its actions based on the success of its reasoning. The system learns to refine its symbolic structures, improving its ability to solve complex problems. This work addresses the limitations of traditional symbolic computation by introducing a dynamic, learning-based framework. The proposed system demonstrates the potential for enhanced reasoning capabilities in domains where symbolic representations are crucial, but static definitions may not be sufficient for optimal performance. The key contributions lie in the synergistic combination of temporal logic's constraint satisfaction capabilities with reinforcement learning's ability to learn optimal strategies. The system's performance is evaluated through simulated scenarios, showcasing the effectiveness of this hybrid approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum circuit design presents significant challenges due to the inherent fragility of quantum states and the complexity of controlling their evolution. Traditional methods often rely on static, pre-defined topologies, limiting the potential for optimization and adaptation to specific quantum systems. This paper introduces a novel approach to quantum circuit design, leveraging adaptive topology – a concept where the circuit's topology dynamically adjusts based on the quantum system's characteristics – to enhance circuit performance and stability. We propose a method for automatically generating and optimizing topological circuits using a reinforcement learning framework. The core mechanism involves a self-adaptive topological graph construction, iteratively refining the circuit's structure to minimize errors and maximize fidelity. The study demonstrates the effectiveness of this approach through simulations and experimental validation on a simplified quantum system, showing significant improvements in performance compared to conventional methods. The paper concludes with a discussion of the potential applications and future research directions in this rapidly evolving field.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to Graph Convolutional Networks (GCNs) that addresses the limitations of static weight assignment in traditional GCNs. We introduce Dynamically Weighted Graph Convolutional Networks (DW-GCNs) which adaptively learn connection weights based on the evolving dynamics of the graph structure and the input features. The core idea is to allow the network to adjust its attention mechanisms, prioritizing more relevant connections at different stages of processing. This dynamic weighting is achieved through a mechanism that implicitly or explicitly learns optimal weights, potentially leveraging reinforcement learning techniques. The resulting DW-GCNs demonstrate improved performance in adaptive feature learning tasks compared to standard GCNs, particularly in scenarios with non-stationary graph structures or varying feature distributions. The key contribution lies in the ability to create a GCN that isn't simply a static aggregation of neighbors but actively responds to the information flow. This work paves the way for more robust and efficient GCN models for various applications, including social network analysis, biological pathway inference, and dynamic sensor networks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Dynamic Neural Topography Learning (NTL), a novel approach to neural network architecture design that leverages the dynamic relationship between physical sensor inputs and the hierarchical structure of a neural network. The core claim is that by dynamically adapting the network's topology based on real-time sensor data, we can significantly enhance its ability to represent time-varying and high-dimensional data. NTL utilizes reinforcement learning to continuously monitor sensor streams and adjust the network's connections and activation thresholds to optimize responsiveness to specific input patterns. A key innovation is the incorporation of a "topology constraint" mechanism, preventing excessive complexity and ensuring the network's adaptability. This approach contrasts with traditional static neural network training methods by dynamically modifying the network's physical structure, offering a more robust solution for dynamic environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the challenges and proposes a novel approach to distributed edge computing resource scheduling and optimization. The core claim is to design an efficient algorithm that dynamically allocates resources based on task requirements and edge computing node capabilities, ultimately enhancing computational efficiency. The proposed method utilizes a reinforcement learning (RL)-based resource scheduling algorithm, learning an optimal resource allocation policy through iterative interaction with the environment. The paper details the algorithm's architecture, the RL framework employed, and presents a theoretical analysis of its performance. The objective is to address the complexities inherent in managing heterogeneous edge environments, optimizing resource utilization, and minimizing latency for applications. Simulation results demonstrate the algorithm's effectiveness in achieving superior performance compared to traditional scheduling approaches. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Hyperparameter tuning is a critical, yet computationally expensive, component of modern machine learning workflows. Traditional Bayesian Optimization (BO) methods, while effective in many scenarios, often struggle when dealing with high-dimensional hyperparameter spaces, leading to slow convergence and suboptimal performance. This paper proposes a novel approach combining Bayesian Optimization with Reinforcement Learning (RL) to address this challenge. The core idea is to leverage an RL agent to learn an optimal exploration strategy for the BO process, dynamically adapting to the specific characteristics of the hyperparameter space. This allows the algorithm to navigate complex landscapes more efficiently and effectively. We formulate the problem as a Markov Decision Process (MDP), where the BO surrogate model acts as the environment, and the RL agent learns to select the next acquisition function based on its observed rewards. The proposed method demonstrates improved performance in high-dimensional hyperparameter tuning tasks compared to standard BO approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Adaptive Stochastic Resonance (ASR) is a novel signal processing technique that dynamically adjusts the stochastic parameters of a signal based on the observed response, resulting in a significant improvement in the signal-to-noise ratio. This paper explores the core mechanism behind ASR, detailing the reinforcement learning algorithm employed for parameter fine-tuning and emphasizing its potential for enhanced signal transmission efficiency. The research focuses on achieving a more robust and adaptable approach compared to traditional signal processing methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Probability Logic Networks (PLN) represent a powerful framework for learning complex patterns from data. Traditional implementations often rely on static network configurations, limiting their ability to adapt to evolving data and improving model generalization. This paper introduces a novel adaptive learning mechanism for PLN, leveraging reinforcement learning to dynamically adjust network structure and weights. The core principle is to train the network through feedback on input data, allowing it to adapt to the specific characteristics of the dataset. This approach significantly enhances feature extraction and improves model robustness. We present a detailed explanation of the mechanism, its implementation, and experimental results demonstrating its effectiveness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel system for non-linear phase transition modeling, designed to automatically tune model parameters based on observed data. Traditional approaches often rely on fixed parameter sets, limiting predictive accuracy. Our system employs a learning mechanism to dynamically adjust model parameters, offering a more flexible and accurate approach to modeling complex phase transition behaviors. We demonstrate the effectiveness of this adaptive tuning through a series of simulations and analysis, showcasing improved predictive performance compared to existing methods. The core concept revolves around a reinforcement learning framework to optimize parameter values while minimizing prediction error. The system is designed to handle a wide range of phase transition models, offering a practical tool for researchers and practitioners alike.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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