Isak Samsten

According to our database, Isak Samsten authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Castor: Competing shapelets for fast and accurate time series classification.

Isak Samsten, Zed Lee

CoRR, 2024

2023

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients.

Zhendong Wang, Isak Samsten, Vasiliki Kougia, Panagiotis Papapetrou

Artif. Intell. Medicine, January, 2023

Code quality assessment using transformers.

Mosleh Mahamud, Isak Samsten

CoRR, 2023

Distributional Data Augmentation Methods for Low Resource Language.

Mosleh Mahamud, Zed Lee, Isak Samsten

CoRR, 2023

Counterfactual Explanations for Time Series Forecasting.

Zhendong Wang, Ioanna Miliou, Isak Samsten, Panagiotis Papapetrou

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective.

Rami Mochaourab, Arun Venkitaraman, Isak Samsten, Panagiotis Papapetrou, Cristian R. Rojas

IEEE Signal Process. Mag., 2022

Corporate governance performance ratings with machine learning.

Jan Svanberg, Tohid Ardeshiri, Isak Samsten, Peter Öhman, Presha E. Neidermeyer, Tarek Rana, Natalia Semenova, Mats Danielson

Intell. Syst. Account. Finance Manag., 2022

2021

Learning Time Series Counterfactuals via Latent Space Representations.

Zhendong Wang, Isak Samsten, Rami Mochaourab, Panagiotis Papapetrou

Proceedings of the Discovery Science - 24th International Conference, 2021

Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions.

Jonathan Rebane, Isak Samsten, Panteleimon Pantelidis, Panagiotis Papapetrou

Proceedings of the 34th IEEE International Symposium on Computer-Based Medical Systems, 2021

Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients.

Zhendong Wang, Isak Samsten, Panagiotis Papapetrou

Proceedings of the Artificial Intelligence in Medicine, 2021

2020

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction.

Jonathan Rebane, Isak Samsten, Panagiotis Papapetrou

Artif. Intell. Medicine, 2020

2019

Example-Based Feature Tweaking Using Random Forests.

Tony Lindgren, Panagiotis Papapetrou, Isak Samsten, Lars Asker

Proceedings of the 20th IEEE International Conference on Information Reuse and Integration for Data Science, 2019