This paper introduces a novel dynamic probabilistic graph model, leveraging high-dimensional neural networks, for natural language processing. We propose a mechanism that dynamically adjusts the network parameters to enhance both semantic reasoning and knowledge graph construction capabilities. The core challenge lies...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to logic programming that integrates neural networks with symbolic reasoning through the dynamic construction and evolution of a knowledge graph. The core idea is to leverage the pattern recognition capabilities of neural networks to learn logical rules and subsequently apply these...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning representations from graph-structured data, finding applications in diverse domains like social network analysis, drug discovery, and recommendation systems. However, training GNNs often requires access to sensitive data, raising significant priv...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embeddings have become a cornerstone of modern graph analysis, enabling the representation of graph structures in continuous vector spaces. However, traditional graph embedding methods often struggle to effectively capture complex dependencies, particularly long-range dependencies and hierarchical relationships i...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper introduces a novel neural network architecture, termed "Adaptive Graph Neural Network" (AGN), that leverages dynamic graph structure learning to enhance data modeling and information extraction. Traditional neural networks typically employ static graph structures, limiting their ability to effectively captur...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to constructing knowledge graphs from implicit, multi-modal data. Traditional knowledge graph construction heavily relies on manual annotation, a process that is both time-consuming and expensive. Our method leverages the rich information contained within user behavior data – encomp...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing Relational Neural Networks (R-Nets) demonstrate effectiveness in various graph-based learning tasks; however, their performance often degrades significantly when applied to very large and complex graphs. This paper introduces a novel approach, Hierarchical Graph Decomposition (HGD), integrated within R-Net arc...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Graph Neural Networks (GNNs) termed Contextual Graph Neural Networks with Memory Resonance (CGNMR). The core idea is that GNNs can significantly benefit from incorporating a mechanism that allows nodes to 'remember' and react to previously observed patterns, effectively propaga...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the integration of high-dimensional data structures and graph neural networks to enhance data analysis and modeling capabilities. The core challenge lies in leveraging the strengths of both paradigms – the inherent connectivity of graphs and the ability of neural networks to learn complex patterns –...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to knowledge graph reasoning utilizing dynamic graph structures. Traditional knowledge graph representation often struggles with the inherent uncertainty and evolving nature of real-world knowledge. We propose a framework that represents knowledge graphs as dynamic graphs, leveragin...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to constructing knowledge graphs from implicit, multi-modal data. Traditional knowledge graph construction heavily relies on manual annotation, a process that is both time-consuming and expensive. Our method leverages the rich information contained within user behavior data – encomp...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to understanding and predicting complex system dynamics by leveraging the principles of self-organization and multi-scale event propagation. The core idea is to construct a self-organized event graph that mimics the behavior of biological neural networks, specifically focusing on th...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
