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Author

Jincheng Zhang

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108 papers indexed here

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#graph neural networks Open access Sep 2026

Core Claim: Design a self-adaptive graph neural network that dynamically adjusts graph structure based on noise and uncertainty, thereby enhancing the performance of graph neural networks.

Graph neural networks (GNNs) have emerged as a powerful tool for various machine learning tasks involving graph-structured data. However, traditional GNN architectures often struggle with noisy and uncertain graph environments, limiting their performance. This paper proposes a novel self-adaptive graph neural network t...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Topological Dependency Graph Learning (DTTL)

This paper introduces Dynamic Topological Dependency Graph Learning (DTTL), a novel approach to program dependency analysis that leverages reinforcement learning to automatically construct and refine dependency graphs without manual intervention. Traditional static dependency analysis tools are limited in their ability...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

基于多智能体协作的复杂系统模拟

This paper presents a novel approach to simulating complex systems by leveraging the power of multi-agent collaboration. The core idea is to construct a population of intelligent agents, each with varying levels of cognitive ability, and task them with solving complex problems collectively. Reinforcement learning is em...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Resource Allocation in Serverless Architectures via Reinforcement Learning

Serverless computing architectures offer significant advantages in terms of scalability and cost-efficiency. However, traditional serverless resource allocation strategies often rely on static configurations or simple heuristics, which can lead to suboptimal performance and increased costs. This paper proposes a novel...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Topological Dependency Reasoning Network (DTTRN)

This paper introduces the Dynamic Topological Dependency Reasoning Network (DTTRN), a novel neural architecture designed for real-time learning and adaptation of complex system topology dependencies. Traditional dependency analysis methods rely on static, pre-defined topologies, limiting their effectiveness in dynamic...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

##基于生物反馈的机器学习模型优化

This paper explores the novel approach of utilizing biofeedback signals, specifically electroencephalogram (EEG) and electrocardiogram (ECG) data, to optimize the performance of machine learning models. The core idea is to leverage the dynamic and often subtle information contained within these physiological signals as...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Adaptive Recursive Neural Network Architecture

This paper proposes a novel adaptive recursive neural network (ARNN) architecture designed to overcome the limitations of traditional, static recurrent neural networks. The core idea is to introduce dynamic adaptation of the network's recursive structure, leveraging evolutionary algorithms or reinforcement learning, to...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

A Cognitive Architecture-Based General Artificial Intelligence Model

This paper proposes a novel approach to developing General Artificial Intelligence (GAI) by leveraging cognitive architectures. The core idea is to model human cognition—encompassing perception, memory, reasoning, learning, and decision-making—within a computational framework. This architecture is built upon a modular...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Automated Theorem Proving with Deep Reinforcement Learning

Automated theorem proving (ATP) has achieved significant progress over the years, yet it frequently struggles with complex mathematical reasoning and strategic planning. This work introduces a novel approach to ATP leveraging deep reinforcement learning (DRL). The core idea is to train an intelligent agent capable of i...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

##基于强化学习的分布式系统资源调度

This paper investigates the application of Reinforcement Learning (RL) for dynamic resource scheduling in distributed systems. Traditional resource scheduling methods often rely on static rules or heuristics, which may not adapt effectively to the fluctuating demands and workloads of a distributed environment. This res...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Title: Dynamically Reconfiguring Quantum Circuit Optimization

Quantum computing holds immense promise for revolutionizing various fields, but the design and optimization of quantum circuits remain a significant challenge. Current methods often rely on manual tweaking and are limited by the time and expertise required. This paper introduces a novel system for dynamically reconfigu...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Title: Adaptive Quantum State Collapse for Quantum Simulation

Quantum simulation, the process of mimicking the behavior of quantum systems, holds immense promise for advancing scientific discovery in fields ranging from materials science to drug discovery. However, traditional quantum simulation methods often face challenges related to computational cost and fidelity, particularl...

Jincheng Zhang · 0 citations

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