39 citations · 105 across the 11 of their papers we have counts for
11 papers
Riemannian Metric Matching for Scalable Geometric Modeling of Distributions
Jacob Bamberger, Adam Gosztolai, Pierre Vandergheynst +2
High-dimensional datasets often concentrate near low-dimensional structures, but estimating their geometry from samples typically relies on graphs and kernels that scale poorly wit…
Diffusion Processes on Implicit Manifolds
Victor Kawasaki-Borruat, Clara Grotehans, Pierre Vandergheynst +1
High-dimensional data are often assumed to lie on lower-dimensional manifolds. We study how to construct diffusion processes on this data manifold using only point cloud samples an…
Carré du champ flow matching: better quality-generalisation tradeoff in generative models
Jacob Bamberger, Iolo Jones, Dennis Duncan +3
Deep generative models often face a fundamental tradeoff: high sample quality can come at the cost of memorisation, where the model reproduces training data rather than generalisin…
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
Robert L. Peach, Matteo Vinao-Carl, Nir Grossman +6
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have exten…
Interpretable statistical representations of neural population dynamics and geometry
Adam Gosztolai, Robert L. Peach, Alexis Arnaudon +2
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretab…
PyGenStability: Multiscale community detection with generalized Markov Stability
Alexis Arnaudon, Juni Schindler, Robert L. Peach +4
We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs…
Connecting the dots in ethology: applying network theory to understand neural and animal collectives
Adam Gosztolai, Pavan Ramdya
A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons a…
Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature
Adam Gosztolai, Alexis Arnaudon
Describing networks geometrically through low-dimensional latent metric spaces has helped design efficient learning algorithms, unveil network symmetries and study dynamical networ…
Cellular memory enhances bacterial chemotactic navigation in rugged environments
Adam Gosztolai, Mauricio Barahona
The response of microbes to external signals is mediated by biochemical networks with intrinsic time scales. These time scales give rise to a memory that impacts cellular behaviour…
Collective search with finite perception: transient dynamics and search efficiency
Adam Gosztolai, Jose A. Carrillo, Mauricio Barahona
Motile organisms often use finite spatial perception of their surroundings to navigate and search their habitats. Yet standard models of search are usually based on purely local se…
GlnK facilitates the dynamic regulation of bacterial nitrogen assimilation
Adam Gosztolai, Jörg Schumacher, Volker Behrends +5
Ammonium assimilation in E. coli is regulated by two paralogous proteins (GlnB and GlnK), which orchestrate interactions with regulators of gene expression, transport proteins and…