5 citations · 5 across the 5 of their papers we have counts for
7 papers
Machine Learning Multiscale Interactions
Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro +4
Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) mo…
PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction
Àlex Solé, Albert Mosella-Montoro, Joan Cardona +4
Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning…
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
Àlex Solé, Albert Mosella-Montoro, Joan Cardona +4
In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine. ADPs capt…
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters
Albert Mosella-Montoro, Javier Ruiz-Hidalgo
This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without ma…
2D-3D Geometric Fusion Network using Multi-Neighbourhood Graph Convolution for RGB-D Indoor Scene Classification
Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D-3D Fusion stage that combines 3D Geometric Features with…
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification
Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These…