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2,002 papers
This paper presents a novel approach to network anomaly detection that leverages topological feature learning to address the limitations of traditional static feature engineering methods. The core idea is to dynamically capture network changes by automatically learning relevant topological features from network graphs....
Graph representation learning has emerged as a crucial technique in various domains, including social network analysis, drug discovery, and recommendation systems. However, many existing methods struggle to effectively capture long-range dependencies within graphs, leading to suboptimal representations. This paper intr...
Traditional methods for causal discovery often face challenges when dealing with intricate, high-dimensional datasets, frequently failing to accurately capture complex causal relationships. Furthermore, these methods typically lack the capability to simulate the consequences of interventions, hindering the ability to v...
The Semantic Web, characterized by linked data and intricate relationships between entities, presents significant challenges for traditional database systems. Existing graph databases often struggle to effectively perform complex reasoning tasks over this data. This paper proposes a novel approach utilizing Graph Neura...
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This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local an...
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and modeling complex relationships within graphs, finding applications in diverse fields like social network analysis, drug discovery, and computer vision. However, traditional GNN architectures often suffer from limitations in capturing long-ra...
This paper explores a novel approach to knowledge graph reasoning utilizing Graph Neural Networks (GNNs). Traditional knowledge graph reasoning methods often struggle with scalability and accurately capturing complex relationships within vast knowledge bases. This work proposes a framework that represents knowledge gra...
This paper introduces Dynamic Topology Dependency Neural Networks (TDNN), a novel neural network architecture designed to address the limitations of traditional static neural networks when processing dynamic and complex data streams. The core concept of TDNN revolves around a dynamically adaptable neural network topolo...
This paper explores the application of topology-based generative algorithms to neural networks, aiming to design novel and complex architectures through a self-organizing learning paradigm. We propose a graph-based approach where the network's structure is explicitly defined by the topology of the input data, offering...
This paper explores the application of Graph Neural Networks (GNNs) to the problem of program abstraction and optimization. Traditional approaches to program abstraction often rely on manually crafted rules or limited pattern matching, which can be brittle and difficult to scale. This work proposes a novel framework th...
This paper proposes a novel approach to compiler optimization by leveraging the power of Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on hand-crafted heuristics and static analysis, often struggling to capture complex code relationships and achieve optimal performance. We intr...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This p...
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.
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.
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.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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