Search Results for author: Paul Scherer

Found 9 papers, 6 papers with code

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery

1 code implementation20 Nov 2022 Yana Lishkova, Paul Scherer, Steffen Ridderbusch, Mateja Jamnik, Pietro Liò, Sina Ober-Blöbaum, Christian Offen

By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.

Distributed representations of graphs for drug pair scoring

1 code implementation19 Sep 2022 Paul Scherer, Pietro Liò, Mateja Jamnik

In this paper we study the practicality and usefulness of incorporating distributed representations of graphs into models within the context of drug pair scoring.

Transductive Learning

PyRelationAL: a python library for active learning research and development

1 code implementation23 May 2022 Paul Scherer, Thomas Gaudelet, Alison Pouplin, Alice Del Vecchio, Suraj M S, Oliver Bolton, Jyothish Soman, Jake P. Taylor-King, Lindsay Edwards

Active learning (AL) is a sub-field 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

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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