Search Results for author: Paul Scherer

Found 7 papers, 3 papers with code

PyRelationAL: A Library for Active Learning Research and Development

no code implementations23 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.

Active Learning

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

3 code implementations15 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.

Using ontology embeddings for structural inductive bias in gene expression data analysis

no code implementations22 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.

Survival Analysis

Incorporating network based protein complex discovery into automated model construction

no code implementations29 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.

Learning distributed representations of graphs with Geo2DR

1 code implementation12 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.

Graph Classification Language Modelling

Decoupling feature propagation from the design of graph auto-encoders

no code implementations18 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.

Graph Learning Graph Representation Learning +1

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