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2,354 papers
This paper introduces a novel approach to compiler optimization decision modeling by leveraging Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on manually crafted rules and heuristics, often proving inadequate for complex codebases and emerging hardware architectures. This resea...
This paper explores the application of Graph Neural Networks (GNNs) to the fields of program synthesis and optimization. The core idea is to represent program code as graph structures and leverage the power of GNNs to automatically learn the underlying structure and semantics. This approach offers a novel pathway for p...
This paper introduces a novel approach to graph embedding that addresses the limitations of existing methods in capturing the dynamic nature of evolving graphs. Traditional graph embedding techniques often rely on static representations, failing to account for the temporal changes in relationships between nodes. Our pr...
This paper introduces a novel approach to program optimization leveraging the power of Graph Neural Networks (GNNs). The core idea is to represent program abstractions, such as Control Flow Graphs (CFGs) and Data Dependency Graphs (DDGs), as graphs and utilize GNNs to automatically identify optimization opportunities a...
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This paper presents a novel approach to graph neural network (GNN) learning, leveraging an adaptive graph neural network (AGNN) to dynamically learn the graph topology and subsequently utilize this topology for data modeling. Traditional GNNs typically rely on static graph structures, limiting their ability to effectiv...
Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a significant limitation of traditional GNNs is their inability to effectively handle evolving graph structures and temporal dependencies....
Power communication networks in virtual power plants (VPPs) play a critical role in enabling reliable information exchange among distributed energy resources, energy storage systems, controllable loads, and dispatch centers. However, their complex topology, strong inter-node dependency, and hidden fault propagation pat...
Fault diagnosis of rotating machinery is essential for ensuring the reliable and safe operation of industrial equipment. However, imbalanced training data often bias intelligent diagnostic models toward majority classes, resulting in insufficient representation of minority faults. In addition, the high dimensionality a...
This paper integrates multiple UQ baseline methods with physics-informed modeling to systematically investigate how physical constraints influence uncertainty estimation in traffic forecasting and proposes a physics-informed loss function that enhances the model's ability to capture physically consistent dynamics and i...
Reproducible computational workflow for the identification and prioritization of putative non-covalent KEAP1-NRF2 protein-protein interaction inhibitors. The workflow integrates structure-based virtual screening, binding-pocket analysis, molecular docking, MM-GBSA rescoring, molecular dynamics simulations, transfer-lea...
Abstract High-precision 3D reconstruction is essential for assessing external quality and extracting phenotypic parameters of navel oranges. Conventional Structure-from-Motion (SfM) pipelines struggle with the weakly textured characteristics and highly specular surfaces of navel oranges, resulting in incomplete reconst...
Scaling of complementary metal-oxide-semiconductor (CMOS) technology into the deep-nanometre regime has made the simultaneous minimisation of power and propagation delay one of the hardest problems in very-large-scale integration (VLSI) design. Supply voltage, threshold-voltage assignment, transistor sizing, body biasi...
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.
Climate & Sustainability
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