23 citations · 60 across the 36 of their papers we have counts for
8 papers · 1 filter
Aspect-Based Few-Shot Learning
Tim van Engeland, Lu Yin, Vlado Menkovski
We generalize the formulation of few-shot learning by introducing the concept of an aspect. In the traditional formulation of few-shot learning, there is an underlying assumption t…
Understanding complex crowd dynamics with generative neural simulators
Koen Minartz, Fleur Hendriks, Simon Martinus Koop +2
Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, bo…
Topological degree as a discrete diagnostic for disentanglement, with applications to the VAE
Mahefa Ratsisetraina Ravelonanosy, Vlado Menkovski, Jacobus W. Portegies
We investigate the ability of Diffusion Variational Autoencoder (VAE) with unit sphere as latent space to capture topological and geometrical structure and disen…
Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
Pol Timmer, Koen Minartz, Vlado Menkovski
Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science an…
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Yoeri Poels, Koen Minartz, Harshit Bansal +1
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations…
Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials
Fleur Hendriks, Vlado Menkovski, Martin Doškář +2
Soft, porous mechanical metamaterials exhibit pattern transformations that may have important applications in soft robotics, sound reduction and biomedicine. To design these innova…
VADA: a Data-Driven Simulator for Nanopore Sequencing
Jonas Niederle, Simon Koop, Marc Pagès-Gallego +1
Nanopore sequencing offers the ability for real-time analysis of long DNA sequences at a low cost, enabling new applications such as early detection of cancer. Due to the complex n…
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski +1
The ability to efficiently predict adsorption properties of zeolites can be of large benefit in accelerating the design process of novel materials. The existing configuration space…