Nikola Simidjievski

Orcid: 0000-0003-3948-6370

According to our database¹, Nikola Simidjievski authored at least 33 papers between 2015 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification.

[BibT_eX]

[DOI]

IEEE Geosci. Remote. Sens. Lett., 2024

2023

AiTLAS: Artificial Intelligence Toolbox for Earth Observation.

[BibT_eX]

[DOI]

Remote. Sens., May, 2023

Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts.

[BibT_eX]

[DOI]

Jonas Jürß

Lucie Charlotte Magister

Pietro Barbiero

Pietro Liò

Nikola Simidjievski

CoRR, 2023

HEALNet - Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data.

[BibT_eX]

[DOI]

Konstantin Hemker

Nikola Simidjievski

Mateja Jamnik

CoRR, 2023

In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification.

[BibT_eX]

[DOI]

CoRR, 2023

SHARCS: Shared Concept Space for Explainable Multimodal Learning.

[BibT_eX]

[DOI]

Gabriele Dominici

Pietro Barbiero

Lucie Charlotte Magister

Pietro Liò

Nikola Simidjievski

CoRR, 2023

Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs.

[BibT_eX]

[DOI]

CoRR, 2023

ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data.

[BibT_eX]

[DOI]

CoRR, 2023

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data.

[BibT_eX]

[DOI]

CoRR, 2022

Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021.

[BibT_eX]

[DOI]

CoRR, 2022

Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification.

[BibT_eX]

[DOI]

CoRR, 2022

AiTLAS: Artificial Intelligence Toolbox for Earth Observation.

[BibT_eX]

[DOI]

CoRR, 2022

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases.

[BibT_eX]

[DOI]

Bioinform., 2022

Attentional Meta-learners for Few-shot Polythetic Classification.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Heavy-tailed denoising score matching.

[BibT_eX]

[DOI]

Jacob Deasy

Nikola Simidjievski

Pietro Liò

CoRR, 2021

Discovering outliers in the Mars Express thermal power consumption patterns.

[BibT_eX]

[DOI]

CoRR, 2021

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data.

[BibT_eX]

[DOI]

José Antonio Martínez Heras

CoRR, 2021

Attentional meta-learners are polythetic classifiers.

[BibT_eX]

[DOI]

CoRR, 2021

Fuzzy Jaccard Index: A robust comparison of ordered lists.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2021

2020

Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Incorporating network based protein complex discovery into automated model construction.

[BibT_eX]

[DOI]

CoRR, 2020

Equation Discovery for Nonlinear System Identification.

[BibT_eX]

[DOI]

IEEE Access, 2020

On Second Order Behaviour in Augmented Neural ODEs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Constraining Variational Inference with Geometric Jensen-Shannon Divergence.

[BibT_eX]

[DOI]

Jacob Deasy

Nikola Simidjievski

Pietro Lió

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Aiding the Task of Process-Based Modeling with ProBMoTViz.

[BibT_eX]

[DOI]

Gjorgi Peev

Nikola Simidjievski

Saso Dzeroski

Int. J. Web Appl., 2019

Towards reusable process-based models of dynamical systems: A case study in the domain of aquatic ecosystems.

[BibT_eX]

[DOI]

Proceedings of the 42nd International Convention on Information and Communication Technology, 2019

2018

Machine learning for predicting thermal power consumption of the Mars Express Spacecraft.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Decoupling approximation robustly reconstructs directed dynamical networks.

[BibT_eX]

[DOI]

CoRR, 2017

Process-Based Modeling and Design of Dynamical Systems.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2016

Learning Ensembles of Process-Based Models by Bagging of Random Library Samples.

[BibT_eX]

[DOI]

Nikola Simidjievski

Ljupco Todorovski

Saso Dzeroski

Proceedings of the Discovery Science - 19th International Conference, 2016

2015

Predicting long-term population dynamics with bagging and boosting of process-based models.

[BibT_eX]

[DOI]

Nikola Simidjievski

Ljupco Todorovski

Saso Dzeroski

Expert Syst. Appl., 2015

Nikola Simidjievski

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