Category
1,934 papers
This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...
Neuro-Symbolic Reasoning via Dynamic Graph Construction (NSRC-DG) presents a novel approach to reasoning that integrates the strengths of neural networks and symbolic reasoning. Traditional reasoning systems often struggle with complex, nuanced inferences requiring deep understanding and logical deduction. This work in...
This record contains a research paper preprint and its associated PDF document.
Temporal Topology Modeling for Signal Processing addresses the limitations of traditional signal processing methods by introducing a novel temporal topology model. This model leverages the combined power of recurrent neural networks and graph theory to effectively capture the evolution of signal patterns over time. The...
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper introduces a novel approach to graph embedding that explicitly incorporates temporal dynamics and relational causality. Existing graph embedding methods often treat graphs as static structures, neglecting the evolving nature of relationships and the underlying causal mechanisms that govern them. We propose a...
This paper introduces a novel self-adaptive grammar rule engine based on graph neural networks. The engine leverages a graph representation of the input text to automatically infer grammatical rules and generate more accurate translations. We propose a method that dynamically adjusts the graph structure based on contex...
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...
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...
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.
Climate & Sustainability
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
