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2,354 papers
This paper introduces a novel approach to Neural Architecture Search (NAS) that leverages dynamic topology optimization based on dependency relationships. Traditional NAS methods often suffer from inefficiency and limited generalization due to the use of static search spaces and a lack of consideration for intricate la...
This paper presents a novel neural network structure optimization method leveraging adaptive graph neural networks (GNNs). The core challenge in training GNNs lies in the dynamic adjustment of connection weights, which often requires extensive hyperparameter tuning. We introduce a new framework that dynamically adjusts...
Graph neural networks (GNNs) have emerged as a powerful tool for various machine learning tasks involving graph-structured data. However, traditional GNN architectures often struggle with noisy and uncertain graph environments, limiting their performance. This paper proposes a novel self-adaptive graph neural network t...
This dataset contains the data files used to train the Graph Neural Network (GNN) model. Tetrahedral.pt — data corresponding to the quarter-filled triangular lattice. Trigonal.pt — data corresponding to the half-filled triangular lattice. Neel.pt — data corresponding to the half-filled square lattice. The files are sto...
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Three empirical functions for estimating relative SASA—approx_sasa, surface_score, and exp_sasa—using node degree as the sole argument are proposed and validated and shown to be suitable for large-scale pipelines requiring both speed and interpretability.
benchmarks demonstrate that GRACE-OFF achieves higher MD performance than comparable MACE-OFF models in both single and double precision, establishing GRACE-OFF as an accurate and computationally efficient foundation potential for routine simulations of organic liquids and biomolecular systems.
A high-fidelity multi-temporal point cloud updating method integrating semantic, geometric and texture information under a unified semantic-geometric collaborative constraint framework, which provides an effective solution for high-fidelity multi-temporal point cloud updating and supports digital twin city construction...
Findings demonstrate that Phys-GCN provides an accurate and efficient graph-based and physics-informed surrogate for steady flow-field reconstruction on non-uniform, unstructured meshes, thereby laying a solid foundation for future extensions to more complex three-dimensional and compressible flow configurations.
This paper introduces the Dynamic Topological Dependency Reasoning Network (DTTRN), a novel neural architecture designed for real-time learning and adaptation of complex system topology dependencies. Traditional dependency analysis methods rely on static, pre-defined topologies, limiting their effectiveness in dynamic...
This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removi...
The central conclusion is that computational sophistication cannot repair absent chemical selectivity or non-independent validation: credible intelligent sensing requires mechanism-grounded signals, information-appropriate models, and validation at the level of the intended deployment claim.
The proliferation of Internet of Things (IoT) devices has expanded the attack surface for sophisticated, polymorphic malware. Traditional intrusion detection systems (IDSs) and standard graph neural networks (GNNs) struggle with the heterogeneity of IoT traffic, adversarial structural perturbations, and the scarcity of...
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.
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