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1,934 papers
This paper introduces a novel approach to neuro-symbolic reasoning utilizing cognitive graph structures. The core idea is to integrate the strengths of neural networks – their ability to learn complex patterns from data – with symbolic knowledge representations – providing explicit, interpretable reasoning. We propose...
This paper proposes a novel approach to analyzing time series data by combining Dynamic Time Warping (DTW) with Graph Neural Networks (GNNs). The core idea is to leverage DTW for effective feature extraction from time series, followed by a GNN to learn intricate relationships within the extracted representations. Tradi...
This paper proposes a novel approach to program optimization that leverages dynamic graph reasoning based on topology. Traditional static dependency graphs fail to adapt to runtime variations and resource constraints, leading to suboptimal performance. We introduce a system that utilizes a neural network to learn and r...
This paper investigates the representation of information flow within neural networks, proposing that the transmission of information is not a random process but rather governed by a specific topological structure. The core argument centers on the idea that the network's topology dictates the pathways through which inf...
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Reliable data movement is essential to the Worldwide LHC Computing Grid. This project asks whether FTS queues that share a storage endpoint contain useful information about one another’s future state. I process 52,037,899 raw queue records from January 2026 into 23,820,642 sparse queue states sampled every 2 minutes. A...
Simulating quantum many-body ground states is constrained by the exponential scaling of the Hilbert space. Recent approaches rely on massive, parameter-heavy neural networks and resource-intensive Variational Monte Carlo (VMC) techniques. We compare two paradigms applied to the 2D Transverse Field Ising Model (TFIM) on...
Visible light positioning (VLP) is a promising indoor localization technology with strong interference immunity and easy integration with existing infrastructure. However, most existing VLP systems rely on multi-anchor deployments and Received Signal Strength (RSS), which are costly to deploy and remain sensitive to re...
This paper presents a novel approach to digital circuit synthesis leveraging Generative Graph Neural Networks (GNNs). The core claim is that designing complex digital circuits is a significantly time-consuming and iterative process. To address this, we propose a system that utilizes a GNN to automatically generate circ...
This paper presents a novel approach to complex system modeling and simulation utilizing graph neural networks (GNNs). The core focus is to develop a method capable of automatically learning complex relationships within a system, and generating high-fidelity predictions. Traditional methods often struggle to effectivel...
This paper introduces a novel self-adaptive graph neural network (GNN) architecture based on dynamic graph representations. Traditional GNNs often struggle with the inherent limitations of static graphs, hindering their ability to effectively handle complex and dynamic data. Our proposed method leverages a dynamic grap...
Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future gra...
This paper introduces a novel method for complex system modeling based on graph neural networks (GNNs) and adaptive learning. Traditional approaches often require manual design of complex network structures, limiting the model's flexibility. Our method leverages GNNs to automatically learn and update graph structures,...
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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