Category
2,002 papers
This paper introduces a novel approach to graph embedding that leverages the temporal dynamics of graph relationships. Traditional graph embedding techniques often treat graphs as static structures, failing to capture the evolving nature of connections between nodes. Our method, Temporal Graph Embedding via Relational...
This paper proposes a novel approach to program error diagnosis and repair leveraging Graph Neural Networks (GNNs). Traditional methods for software defect detection and correction often rely on manual inspection or rule-based systems, which can be time-consuming, expensive, and prone to human error. Our approach trans...
An edge AI‐powered power converter (EAI‐PC) hardware platform that utilizes locally deployed edge intelligence to establish bidirectional data exchange between converter low‐level control and EAI upper‐layer command dispatch and features an end‐to‐end AI‐to‐converter closed‐loop deployment framework.
Biological regulatory networks (BRNs) govern cellular processes with inherent stochasticity, reflecting the probabilistic nature of gene expression and protein interactions. Traditional Graph Neural Networks (GNNs) often treat these networks as deterministic, leading to inaccurate representations and predictions. This...
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A gra...
This paper explores the development of an emergent semantic network system designed to accelerate and enhance mathematical proof verification. Traditional methods rely heavily on manual verification, which is inherently slow and susceptible to human error. We propose a novel approach leveraging graph neural networks to...
This paper introduces a novel approach to graph embedding that explicitly models the temporal dynamics of relationships within a graph. Current graph embedding techniques often treat graphs as static structures, neglecting the crucial aspect of how connections evolve over time. This work addresses this limitation by le...
The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis...
This paper presents a novel approach to graph neural networks (GNNs) based on adaptive graph structures. Traditional GNNs often employ static graph structures, limiting their ability to effectively capture complex relationships within the data. This research introduces a dynamic graph structure adjustment mechanism, dy...
This study introduces a novel memory-augmented self-learning framework that extracts and provides diverse learning sources for adaptive knowledge distillation from the student model itself, resulting in a 2.5-6% increase in accuracy across various benchmark datasets compared to current GNN training and self-distillatio...
This paper introduces a novel approach to modeling complex systems utilizing multi-scale graph neural networks (MS-GNNs). Complex systems, characterized by interconnected components operating at varying scales, pose significant challenges for traditional modeling techniques. This research proposes a framework that deco...
This paper proposes a novel approach to neural network architecture generation, termed "Neuron Circuitry Driven Dynamic Program Generation." The core claim is that comprehensive analysis of large-scale neuron circuits can automatically generate highly optimized neural network architectures and connection weights tailor...
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.
