Nikola Simidjievski
Cited by
Cited by
Predicting long-term population dynamics with bagging and boosting of process-based models
N Simidjievski, L Todorovski, S Džeroski
Expert Systems with Applications 42 (22), 8484-8496, 2015
Modeling dynamic systems with efficient ensembles of process-based models
N Simidjievski, L Todorovski, S Džeroski
PloS one 11 (4), e0153507, 2016
Variational autoencoders for cancer data integration: design principles and computational practice
N Simidjievski, C Bodnar, I Tariq, P Scherer, H Andres Terre, Z Shams, ...
Frontiers in genetics 10, 1205, 2019
Predicting thermal power consumption of the Mars Express satellite with machine learning
M Breskvar, D Kocev, J Levatić, A Osojnik, M Petković, N Simidjievski, ...
2017 6th International conference on space mission challenges forá…, 2017
Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems
N Simidjievski, L Todorovski, S Džeroski
Ecological Modelling 306, 305-317, 2015
Process-based modeling and design of dynamical systems
J Tanevski, N Simidjievski, L Todorovski, S Džeroski
Joint European Conference on Machine Learning and Knowledge Discovery iná…, 2017
On second order behaviour in augmented neural odes
A Norcliffe, C Bodnar, B Day, N Simidjievski, P Li˛
arXiv preprint arXiv:2006.07220, 2020
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
M Petković, R Boumghar, M Breskvar, S Džeroski, D Kocev, J Levatić, ...
IEEE Aerospace and Electronic Systems Magazine 34 (7), 46-60, 2019
Decoupling approximation robustly reconstructs directed dynamical networks
N Simidjievski, J Tanevski, B Ženko, Z Levnajić, L Todorovski, S Džeroski
New Journal of Physics 20 (11), 113003, 2018
Constraining variational inference with geometric jensen-shannon divergence
J Deasy, N Simidjievski, P Li˛
arXiv preprint arXiv:2006.10599, 2020
Learning ensembles of process-based models by bagging of random library samples
N Simidjievski, L Todorovski, S Džeroski
International Conference on Discovery Science, 245-260, 2016
Quantifying the effects of gyroless flying of the mars express spacecraft with machine learning
M Petkovic, L Lucas, D Kocev, S Džeroski, R Boumghar, N Simidjievski
2019 IEEE International Conference on Space Mission Challenges forá…, 2019
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ...
bioRxiv, 2021
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
A Margeloiu, N Simidjievski, M Jamnik, A Weller
arXiv preprint arXiv:2012.01166, 2020
Using ontology embeddings for structural inductive bias in gene expression data analysis
M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ...
arXiv preprint arXiv:2011.10998, 2020
Equation Discovery for Nonlinear System Identification
N Simidjievski, L Todorovski, J Kocijan, S Džeroski
IEEE Access 8, 29930-29943, 2020
Modeling of dynamical systems: a survey of tools and a case study
G Peev, N Simidjievski, S Džeroski
20th International Multiconference Information Society-IS, 15-18, 2017
Biocircuit Design with Equation Discovery
J Tanevski, N Simidjievski, S Džeroski
Learning and Discovery in Symbolic Systems Biology, 2, 2012
Attentional meta-learners are polythetic classifiers
B Day, R Vi˝as, N Simidjievski, P Li˛
arXiv preprint arXiv:2106.05317, 2021
Incorporating network based protein complex discovery into automated model construction
P Scherer, M Trȩbacz, N Simidjievski, Z Shams, HA Terre, P Li˛, ...
arXiv preprint arXiv:2010.00387, 2020
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