Paul Scherer
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Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models
B Rozemberczki, P Scherer, Y He, G Panagopoulos, A Riedel, ...
Proceedings of the 30th ACM international conference on information …, 2021
Variational autoencoders for cancer data integration: design principles and computational practice
N Simidjievski, C Bodnar, I Tariq, P Scherer, HA Terre, Z Shams, ...
Frontiers in Genetics 10, 2019
Associations between maternal physical activity in early and late pregnancy and offspring birth size: remote federated individual level meta‐analysis from eight cohort studies
S Pastorino, T Bishop, SR Crozier, C Granström, K Kordas, LK Küpers, ...
BJOG: An International Journal of Obstetrics & Gynaecology 126 (4), 459-470, 2019
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
B Rozemberczki, P Scherer, O Kiss, R Sarkar, T Ferenci
WWW’21: Graph Learning Benchmarks Workshop, 2021
REM: An integrative rule extraction methodology for explainable data analysis in healthcare
Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ...
medRxiv, 2021.01. 25.21250459, 2021
Learning distributed representations of graphs with Geo2DR
P Scherer, P Lio
Graph Representation Learning and Beyond Workshop (ICML'20), 2020
Discrete Lagrangian neural networks with automatic symmetry discovery
Y Lishkova, P Scherer, S Ridderbusch, M Jamnik, P Liň, S Ober-Blöbaum, ...
IFAC-PapersOnLine 56 (2), 3203-3210, 2023
Using ontology embeddings for structural inductive bias in gene expression data analysis
M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ...
15th Machine Learning in Computational Biology (MLCB'20), 2020
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
P Scherer, M Trębacz, N Simidjievski, R Vińas, Z Shams, HA Terre, ...
Bioinformatics 38 (5), 1320-1327, 2022
Distributed representations of graphs for drug pair scoring
P Scherer, P Liň, M Jamnik
Learning on Graphs Conference, 22: 1-22: 17, 2022
Decoupling feature propagation from the design of graph auto-encoders
P Scherer, H Andres-Terre, P Lio, M Jamnik
arXiv preprint arXiv:1910.08589, 2019
Distributional and relational inductive biases for graph representation learning in biomedicine
P Scherer
Spatio-relational inductive biases in spatial cell-type deconvolution
R Vinas, P Scherer, N Simidjievski, M Jamnik, P Lio
bioRxiv, 2023.05. 19.541474, 2023
PyRelationAL: a python library for active learning research and development
P Scherer, T Gaudelet, A Pouplin, A Del Vecchio, O Bolton, J Soman, ...
arXiv preprint arXiv:2205.11117, 2022
PyRelationAL: A Library for Active Learning Research and Development.
P Scherer, T Gaudelet, A Pouplin, MS Suraj, J Soman, L Edwards, ...
CoRR, 2022
Incorporating network based protein complex discovery into automated model construction
P Scherer, M Trȩbacz, N Simidjievski, Z Shams, H Andres Terre, P Liň, ...
15th Machine Learning in Computational Biology (MLCB'20), 2020
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