1 code implementation • 28 Sep 2023 • Robert L. Peach, Matteo Vinao-Carl, Nir Grossman, Michael David, Emma Mallas, David Sharp, Paresh A. Malhotra, Pierre Vandergheynst, Adam Gosztolai
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data.
1 code implementation • NeurIPS 2023 • Zhaolu Liu, Robert L. Peach, Pedro A. M. Mediano, Mauricio Barahona
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems.
1 code implementation • 15 May 2023 • Zhaolu Liu, Robert L. Peach, Felix Laumann, Sara Vallejo Mengod, Mauricio Barahona
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas.
1 code implementation • 6 Apr 2023 • Adam Gosztolai, Robert L. Peach, Alexis Arnaudon, Mauricio Barahona, Pierre Vandergheynst
The dynamics of neuron populations during many behavioural tasks evolve on low-dimensional manifolds.
1 code implementation • 24 Sep 2019 • Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples.