1 code implementation • 19 Mar 2024 • Isak Samsten, Zed Lee
The transformation organizes shapelets into groups with varying dilation and allows the shapelets to compete over the time context to construct a diverse feature representation.
1 code implementation • 12 Oct 2023 • Zhendong Wang, Ioanna Miliou, Isak Samsten, Panagiotis Papapetrou
In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series.
no code implementations • 17 Sep 2023 • Mosleh Mahamud, Isak Samsten
Automatically evaluate the correctness of programming assignments is rather straightforward using unit and integration tests.
1 code implementation • 9 Sep 2023 • Mosleh Mahamud, Zed Lee, Isak Samsten
One of the current state-of-the-art text augmentation techniques is easy data augmentation (EDA), which augments the training data by injecting and replacing synonyms and randomly permuting sentences.
1 code implementation • Artificial Intelligence in Medicine 2023 • Zhendong Wang, Isak Samsten, Vasiliki Kougia, Panagiotis Papapetrou
In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique.
1 code implementation • International Conference on Discovery Science 2021 • Zhendong Wang, Isak Samsten, Rami Mochaourab, Panagiotis Papapetrou
Counterfactual explanations can provide sample-based explanations of features required to modify from the original sample to change the classification result from an undesired state to a desired state; hence it provides interpretability of the model.
1 code implementation • International Conference on Artificial Intelligence in Medicine 2021 • Zhendong Wang, Isak Samsten, Panagiotis Papapetrou
In recent years, machine learning methods have been rapidly implemented in the medical domain.