Nataliya Sokolovska
Orcid: 0000-0001-8841-1725
According to our database1,
Nataliya Sokolovska
authored at least 42 papers
between 2008 and 2024.
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Bibliography
2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework.
BMC Bioinform., December, 2023
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells.
J. Chem. Inf. Model., November, 2023
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
2022
Learning sparse representations of preferences within Choquet expected utility theory.
Proceedings of the Uncertainty in Artificial Intelligence, 2022
2021
Pattern Recognit. Lett., 2021
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism.
Entropy, 2021
2020
Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the σ-phase as an example.
CoRR, 2020
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism.
CoRR, 2020
A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.
Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020
Proceedings of The 12th Asian Conference on Machine Learning, 2020
2019
Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines.
Inf. Fusion, 2019
Robust structure measures of metabolic networks that predict prokaryotic optimal growth temperature.
BMC Bioinform., 2019
Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks.
Proceedings of the 2019 IEEE-RIVF International Conference on Computing and Communication Technologies, 2019
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks.
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019) Stanford University, 2019
2018
CoRR, 2018
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2018
Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2018
Proceedings of the 26th European Signal Processing Conference, 2018
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks.
CoRR, 2017
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017
2016
Int. J. Approx. Reason., 2016
Spectral consensus strategy for accurate reconstruction of large biological networks.
BMC Bioinform., 2016
Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
A probabilistic prior knowledge integration method: Application to generative and discriminative models.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016
2015
Proceedings of the Advances in Intelligent Data Analysis XIV, 2015
2012
Proceedings of the Neural Information Processing - 19th International Conference, 2012
2011
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011
Proceedings of the Learning and Intelligent Optimization - 5th International Conference, 2011
Proceedings of the Neural Information Processing - 18th International Conference, 2011
Proceedings of the Foundations of Genetic Algorithms, 11th International Workshop, 2011
2010
Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection. (Contributions à l'estimation de modèles probabilistes discriminants: apprentissage semi-supervisé et sélection de caractéristiques).
PhD thesis, 2010
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labeling.
IEEE J. Sel. Top. Signal Process., 2010
Proceedings of the Computers and Games - 7th International Conference, 2010
2009
Trait. Autom. des Langues, 2009
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
CoRR, 2009
2008
Proceedings of the Machine Learning, 2008