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2,280 papers
capture. A reaction-level graph neural network with transfer learning was proposed that efficiently predicted the temperature dependence of the carbonation reaction.
The proposed model combining a long short-term memory backbone, GNSS conditioning, and a graph neural network (GNN), denoted LSTM+GNN+GNSS, for 4 h wind-power forecasting demonstrates the incremental predictive value of the complete GNSS-conditioning pathway under the chronological holdout protocol.
In recent times, power grid optimization has incorporated ML and optimization algorithms to improve the power grid and reduce losses. Graph Neural Networks (GNN) are efficient in modelling complex dependencies, while Particle Swarm Optimization (PSO) optimizes line losses and operating costs. However, the application o...
Subtype selectivity across human adenosine GPCRs remains an intractable medicinal chemistry challenge. The four orthosteric binding pockets share over 70% sequence homology across transmembrane helices III, V, VI, and VII. Consequently, standard QSAR models fail prospectively. They suffer from systemic scaffold leakage...
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Accurate topology awareness is a prerequisite for automation-control-oriented monitoring of complex power grids that support electrified transportation loads, distributed energy resources, and frequent switching operations. This paper proposes an edge-gated graph neural network (GNN) framework for joint topology recogn...
Practical Graph Intelligence 2 delivers a comprehensive and application driven exploration of graph-based methods for understanding complex, interconnected data. This book bridges theory and practice by presenting advanced techniques in graph theory, graph neural networks and network analytics, with a strong focus on r...
This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled: "Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction" The provided materials support the implementation and eval...
Real-time product carbon footprint (PCF) reporting is increasingly required in global supply chains, yet conventional life cycle assessment (LCA) remains difficult to scale due to fragmented multitier data, heterogeneous formats, and substantial manual effort. This article proposes Auto-LCA, a system-level architecture...
Hyperspectral images contain abundant spectral information and provide fine-grained spatial representations. However, their high dimensionality, severe spectral redundancy, subtle inter-class differences, mixed boundary regions, and limited labeled samples pose significant challenges to accurate classification. Convolu...
This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled: "Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction" The provided materials support the implementation and eval...
We present a live performance for experimental electronic music in which a continuously evolving neural network joins two human performers to form an improvising trio. Rather than training a generative model on pre-existing works, we use neuroevolution strategies to grow a feed-forward neural graph in real time, with n...
Abstract Active learning provides an efficient strategy for molecular property prediction by iteratively prioritizing compounds for experimental evaluation. However, the effectiveness of active learning pipelines depends strongly on the choice of molecular representation, and systematic understanding of how representat...
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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