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Found 7 papers, 6 papers with code
Castor: Competing shapelets for fast and accurate time series classification
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.
Counterfactual Explanations for Time Series Forecasting
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.
Code quality assessment using transformers
Automatically evaluate the correctness of programming assignments is rather straightforward using unit and integration tests.
Distributional Data Augmentation Methods for Low Resource Language
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.
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients
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.
Learning Time Series Counterfactuals via Latent Space Representations
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.
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients
In recent years, machine learning methods have been rapidly implemented in the medical domain.
