25 citations · 88 across the 11 of their papers we have counts for
11 papers
ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeuti…
A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur, Felix Laumann, Junhyung Park +1
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions. As an extension to the graph-ba…
Analysing Multiscale Clusterings with Persistent Homology
Juni Schindler, Mauricio Barahona
In data clustering, it is often desirable to find not just a single partition into clusters but a sequence of partitions that describes the data at different scales (or levels of c…
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…
Deep incremental learning models for financial temporal tabular datasets with distribution shifts
Thomas Wong, Mauricio Barahona
We present a robust deep incremental learning framework for regression tasks on financial temporal tabular datasets which is built upon the incremental use of commonly available ta…
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…
Online learning techniques for prediction of temporal tabular datasets with regime changes
Thomas Wong, Mauricio Barahona
The application of deep learning to non-stationary temporal datasets can lead to overfitted models that underperform under regime changes. In this work, we propose a modular machin…
Finding positively invariant sets and proving exponential stability of limit cycles using Sum-of-Squares decompositions
Elias August, Mauricio Barahona
The dynamics of many systems from physics, economics, chemistry, and biology can be modelled through polynomial functions. In this paper, we provide a computational means to find p…
Sensitivity and spectral control of network lasers
Dhruv Saxena, Alexis Arnaudon, Oscar Cipolato +7
Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled,…
Multiscale mobility patterns and the restriction of human movement
Juni Schindler, Jonathan Clarke, Mauricio Barahona
From the perspective of human mobility, the COVID-19 pandemic constituted a natural experiment of enormous reach in space and time. Here, we analyse the inherent multiple scales of…
Relative, local and global dimension in complex networks
Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical…