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1,904 papers
Graph Neural Networks (GNNs) have achieved remarkable success in various domains, including node classification, graph classification, and link prediction. However, their large size and high computational cost pose significant challenges for deployment, particularly on resource-constrained devices. Graph Neural Network...
Graph representation learning has emerged as a powerful technique for analyzing and understanding complex data represented as graphs. Traditional Graph Neural Networks (GNNs) typically learn static representations of nodes, often neglecting the evolving nature of relationships within the graph. This work introduces a n...
This paper proposes a novel approach to learning complex functions and representations by integrating Hypergraph Neural Networks (HNNs) with Symbolic Regression (SR). The core idea is to leverage the graph representation capabilities of HNNs to extract informative features from hypergraph data, and then utilize a symbo...
Machine learning models for toxicity prediction are routinely evaluated using random train/test splits, allowing structurally similar compounds to appear in both sets and inflating reported performance metrics. A rigorous benchmark quantifying this overestimation alongside calibration, uncertainty, and applicability do...
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Abstract Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heterogeneous graph representation usually depend on the computation of specific, user-defined heterogeneous paths, or on the applicatio...
Graph-based network intrusion detection almost universally represents attacker behavior through node-centric structural features – degree, centrality, embeddings from graph neural networks – which implicitly assume that an attacker’s identity is visible as a distinct node in the host-communication graph. We show that t...
Dynamical Topology Optimization (DTO) provides a framework for optimizing complex network topologies by iteratively refining connections based on emergent properties. This paper introduces a novel algorithm, termed 'Adaptive Topology Evolution', which integrates reinforcement learning and graph theory to achieve automa...
Tri-hybrid multiple-input multiple-output architectures have recently emerged as a promising enabler for next-generation wireless systems, as they potentially provide enhanced design flexibility without a proportional increase in hardware cost or power consumption. However, the resulting triple-domain coupling renders...
Programmable quantum annealers can realize real-time dynamics of frustrated transverse-field Ising models on large, nontrivial graphs. Recent work by King et al. argued that the classical simulation of such experiments would require exponential computational resources for classical methods such as tensor networks and n...
A two-axis taxonomy connecting six compression families to five semantic communication architectures is synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier, synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier.
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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