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2,354 papers
This paper investigates the application of neuromorphic computing architectures for real-time graph processing. Traditional graph processing relies on von Neumann architectures, which suffer from inherent bottlenecks due to the separation of processing and memory. We propose a novel approach utilizing spiking neural ne...
Existing graph embedding techniques predominantly focus on static graph structures, neglecting the crucial aspect of temporal dynamics inherent in many real-world networks. This paper introduces a novel approach – Temporal Graph Embedding with Relational Dynamics – that addresses this limitation. Our method leverages a...
This paper introduces a novel dynamic graph neural network (D-GNN) architecture, termed "Adaptive Dynamic Graph Neural Network," designed to optimize the learning process of GNNs through dynamic adjustment of the graph's structure and parameters. Traditional GNN approaches often rely on fixed graph structures, limiting...
This paper introduces a novel approach to modeling social network dynamics using Hypergraph Neural Networks (HNNs). Traditional Graph Neural Networks (GNNs) struggle to accurately represent and propagate information through networks exhibiting higher-order relationships, a common characteristic of social structures. HN...
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This paper investigates the potential of topological association mapping (TAM) to accurately decode and reconstruct complex neuronal memories. The core claim is that analyzing the dynamic topological association network between neurons provides a more precise method than traditional approaches. We propose a framework u...
This paper introduces a novel approach to graph embedding that combines the strengths of graph neural networks (GNNs) with symbolic representation learning. The core idea is to develop a system capable of learning both the structural and relational aspects of a graph through a dual mechanism. The system utilizes a GNN...
This paper introduces an Adaptive Graph Neural Network (AGNN) designed to leverage geometric information for enhanced feature extraction and learning. Traditional graph neural networks (GNNs) often rely solely on data, limiting their ability to exploit the inherent structure of the data. This research proposes a novel...
This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as gr...
This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research intro...
This paper introduces a novel approach to multimodal data fusion utilizing graph-to-graph models. The core idea is to represent diverse data modalities—including text, images, and audio—as interconnected nodes within a graph structure. This graph facilitates the fusion of information and enables reasoning across modali...
This work develops the principle that choosing a neural architecture is not a matter of choosing the largest model, but of choosing an inductive bias that matches the structure the data already has through three self-contained case studies.
This paper proposes a novel approach to software component recommendation utilizing dynamic graph neural networks (DGNNs). Traditional software component recommendation methods often rely on static graphs or keyword-based searches, which fail to effectively capture the dynamic relationships and usage patterns inherent...
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
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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