Nikola Simidjievski
Cited by
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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
On second order behaviour in augmented neural odes
A Norcliffe, C Bodnar, B Day, N Simidjievski, P Li˛
Advances in Neural Information Processing Systems 33, 5911-5921, 2020
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
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
Constraining variational inference with geometric jensen-shannon divergence
J Deasy, N Simidjievski, P Li˛
Advances in Neural Information Processing Systems 33, 10647-10658, 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
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
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
Improving interpretability in medical imaging diagnosis using adversarial training
A Margeloiu, N Simidjievski, M Jamnik, A Weller
arXiv preprint arXiv:2012.01166, 2020
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
Equation discovery for nonlinear system identification
N Simidjievski, L Todorovski, J Kocijan, S Džeroski
IEEE Access 8, 29930-29943, 2020
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
Fuzzy jaccard index: a robust comparison of ordered lists
M Petković, B Škrlj, D Kocev, N Simidjievski
Applied Soft Computing 113, 107849, 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
Heavy-tailed denoising score matching
J Deasy, N Simidjievski, P Li˛
arXiv preprint arXiv:2112.09788, 2021
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
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
I Dimitrovski, I Kitanovski, P Panov, N Simidjievski, D Kocev
arXiv preprint arXiv:2201.08789, 2022
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
Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases
P Scherer, M Trębacz, N Simidjievski, R Vi˝as, Z Shams, HA Terre, ...
Bioinformatics 38 (5), 1320-1327, 2022
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