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1,904 papers
This paper explores the application of Graph Neural Networks (GNNs) to program structure optimization. Traditional program optimization techniques often rely on static analysis and hand-crafted rules, which can be limited in their ability to capture complex code dependencies and adapt to diverse program styles. We prop...
This paper presents a novel approach to intelligent perception systems by leveraging multi-scale information fusion. The core idea is to effectively integrate diverse data modalities – including low-resolution, high-resolution images, audio, and text – represented through a graph-based framework. This framework utilize...
This paper introduces the Dynamic Semantic Embedding Network (DSEN), a novel approach to program understanding and prediction that leverages the power of graph neural networks and continuous learning. The core claim of DSEN is to achieve dynamic comprehension and prediction of program behavior without explicit annotati...
Knowledge graph completion (KGC) is a critical task in leveraging the rich structural information embedded within knowledge graphs (KGs). Traditional graph neural networks (GNNs) often face challenges in effectively capturing the intricate relationships and dependencies within these graphs, particularly when dealing wi...
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This paper proposes a novel approach to semantic understanding utilizing multi-scale graph neural networks (MS-GNNs). Traditional semantic understanding models often operate within a single scale of representation, limiting their ability to capture nuanced relationships and contextual information. Our method addresses...
This paper introduces a novel approach to knowledge graph reasoning based on Dynamic Graph Neural Networks (DGNNs). Traditional knowledge graph reasoning models often treat knowledge graphs as static structures, failing to capture the inherent dynamic evolution of entities and relationships. This work addresses this li...
This paper presents a novel approach to verifying complex temporal logic specifications on dynamic systems represented as graphs, addressing the significant computational challenges inherent in traditional verification methods. The core idea is to leverage the scalability of Probabilistic Neural Networks (PNNs) to appr...
Graph representation learning has achieved significant success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a critical limitation often exists when dealing with temporal data embedded within graphs – accurately capturing the evolving relationship...
The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph...
Graph representation learning has emerged as a powerful technique for analyzing complex relationships within graph-structured data. However, existing methods often struggle to effectively capture the temporal dynamics inherent in many real-world graphs, where relationships evolve over time. This research introduces a n...
This paper introduces a novel approach to graph theory based on neural rings – a dynamic neural network representation of graph structure. The core innovation lies in employing a neural ring that learns and adjusts its parameters to optimize the graph's inherent properties, leading to enhanced information propagation e...
This paper investigates a novel dynamic approach to adaptive neural network weight adjustment, specifically focusing on the utilization of graph neural networks (GNNs) for dynamic node weighting. We introduce a method that intelligently adjusts node weights during training based on the network's internal state and data...
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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