no code implementations • 23 May 2022 • Paul Scherer, Thomas Gaudelet, Alison Pouplin, Suraj M S, Jyothish Soman, Lindsay Edwards, Jake P. Taylor-King
Active learning (AL) is a subfield of ML focused on the development of methods to iteratively and economically acquire data through strategically querying new data points that are the most useful for a particular task.
3 code implementations • 15 Apr 2021 • Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
3 code implementations • 16 Feb 2021 • Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
no code implementations • 22 Nov 2020 • Maja Trębacz, Zohreh Shams, Mateja Jamnik, Paul Scherer, Nikola Simidjievski, Helena Andres Terre, Pietro Liò
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
no code implementations • 29 Sep 2020 • Paul Scherer, Maja Trȩbacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Liò, Mateja Jamnik
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.
1 code implementation • 12 Mar 2020 • Paul Scherer, Pietro Lio
We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and neural language models.
no code implementations • 18 Oct 2019 • Paul Scherer, Helena Andres-Terre, Pietro Lio, Mateja Jamnik
We present two instances, L-GAE and L-VGAE, of the variational graph auto-encoding family (VGAE) based on separating feature propagation operations from graph convolution layers typically found in graph learning methods to a single linear matrix computation made prior to input in standard auto-encoder architectures.