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1,966 papers
This work develops the principle that choosing a neural architecture is not a matter of choosing the largest model, but of choosing an inductive bias that matches the structure the data already has through three self-contained case studies.
This paper proposes a novel approach to software component recommendation utilizing dynamic graph neural networks (DGNNs). Traditional software component recommendation methods often rely on static graphs or keyword-based searches, which fail to effectively capture the dynamic relationships and usage patterns inherent...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, recommendation systems, and molecular property prediction. However, a key limitation of many GNN architectures lies in their static neighborhood aggregation strategy, often relying on fixed neighborh...
This paper explores the application of Hierarchical Graph Neural Networks (HGN) to modeling and understanding complex systems. Traditional graph neural networks often treat the entire graph as a single entity, limiting their ability to capture the inherent hierarchical structure and emergent properties of these systems...
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This paper investigates the application of Quantum Adaptive Algorithms (QAA) to Graph Neural Networks (GNNs). We propose a novel QAGAN architecture that leverages quantum self-optimization to enhance the training and inference process of GNNs. The core of this approach is a dynamically adjusted parameter set derived fr...
This paper proposes a novel framework for constructing and reasoning with dynamic semantic graph knowledge. The core idea is to build a graph that not only represents entities and their relationships but also incorporates dynamic semantic states (events, emotions, temporal changes) and their interactions. This dynamic...
This paper explores the application of Graph Neural Networks (GNNs) to social network analysis. Traditional social network analysis methods often struggle to capture complex, hidden relationships and patterns within networks, particularly in large and dynamic social structures. GNNs offer a novel approach by directly l...
This paper investigates the development of a novel data structure auto-generation system utilizing machine learning. Recognizing the challenges posed by high-dimensional data, this research proposes a method for automatically constructing data structures like matrices and graphs, thereby simplifying data processing and...
This paper investigates the application of multi-modal collaborative learning for latent variable discovery. Traditional methods for uncovering hidden variables predominantly focus on single modality data, often overlooking the synergistic information present when multiple modalities are available. We propose a novel f...
This paper introduces Temporal Graph Neural Networks (TGNNs), a novel approach to graph neural networks designed to handle dynamic graphs where relationships evolve over time. Traditional Graph Neural Networks (GNNs) operate on static graphs, failing to capture the inherent temporal dependencies within systems. TGNNs a...
GradTDDFT v1.1.0 GradTDDFT v1.1.0 provides a JAX-based framework for differentiable DFT, TDA/TDDFT, and neural exchange-correlation functional training. Highlights PySCF-aligned numerical grids: The standalone JAX grid now matches PySCF-style Lebedev quadrature, Treutler–Ahlrichs radial grids, NWChem pruning, and Becke...
This repository contains the analytical code for the manuscript, Benchmarking predictive architectures for meningitis forecasting and outbreak detection using environmental drivers. It provides the complete Python and PyTorch codebase to replicate our multi-architecture evaluation of early warning systems for epidemic...
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.
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