20 citations · 36 across the 10 of their papers we have counts for
5 papers · 1 filter
Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture
Tien Comlekoglu, J. Quetzalcóatl Toledo-Marín, Tina Comlekoglu +4
The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (…
Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breaking
J. Quetzalcóatl Toledo-Marin, Anindita Maiti, Geoffrey C. Fox +1
Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationship…
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
J. Quetzalcoatl Toledo-Marin, Sebastian Gonzalez, Hao Jia +9
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Mod…
Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation
J. Quetzalcoatl Toledo-Marin, James A. Glazier, Geoffrey Fox
Neural networks (NNs) have proven to be a viable alternative to traditional direct numerical algorithms, with the potential to accelerate computational time by several orders of ma…
Using Deep LSD to build operators in GANs latent space with meaning in real space
J. Quetzalcoatl Toledo-Marin, James A. Glazier
Generative models rely on the key idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation is important because it s…