Category
1,934 papers
This paper proposes a novel approach to program code semantic understanding and reconstruction leveraging Graph Neural Networks (GNNs). The core idea is to directly learn semantic relationships within program code by representing it as a graph structure. Nodes in the graph represent code elements such as functions, var...
This paper introduces a novel graph neural network (GNN) architecture, termed "Adaptive Graph Neural Network" (AGNN), designed to dynamically adjust its graph structure in response to data fluctuations. The AGNN's core mechanism centers around a dynamic parameterization of the graph, enabling efficient knowledge transf...
This paper investigates the application of Graph Neural Networks (GNNs) to dynamic path planning, offering a novel approach to address the limitations of traditional methods. Dynamic path planning necessitates the ability to adapt to changing environmental conditions and real-time data, which is often challenging with...
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper introduces a novel approach to geometric modeling that leverages graph neural networks (GNNs) for adaptive complexity learning. Traditional methods often require manual design of complex geometric models, leading to high modeling costs. We propose a system that dynamically adjusts the complexity of GNNs base...
This paper introduces a novel system of framework for predicting system states and enabling collaborative control utilizing graph neural networks (GNNs). The core idea is to leverage graph structure to model system dynamics, identify key nodes and their relationships, and facilitate efficient control strategies. We pro...
This paper introduces a novel framework for complex system modeling based on graph neural networks (GNNs). We propose a method to automatically learn complex system structures from data, facilitating efficient complexity analysis. Traditional approaches often require manual model design, while GNNs offer a powerful mec...
Findings show that under the evaluated setting, greater model complexity did not consistently translate into higher recommendation effectiveness, and they thus highlight the importance of strong baselines, model tuning, standardized evaluation, and reproducible experimental protocols.
Abstract Epilepsy ranks among the most prevalent and debilitating neurological disorders, globally affecting an estimated 50 million individuals across all age groups and socioeconomic backgrounds [WHO, 2019]. In recent years, research on its prediction methods has made significant progress, driven by advancements in a...
Activated Permutation Entropy is introduced and the development of improved graph-based entropy methods that enable nonlinear analysis of broader data types and establishing relationships with emerging ideas in data science is expected to accelerate.
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.
