English
norskBlar i forfatter "Boubekki, Ahcene"
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Clinically relevant features for predicting the severity of surgical site infections
(Journal article; Tidsskriftartikkel, 2021)Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who ... -
A Contextually Supported Abnormality Detector for Maritime Trajectories
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-31)The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising ... -
Joint optimization of an autoencoder for clustering and embedding
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-21)Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to beneft from valuable information acquired by the latter. In ... -
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-15)The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the predictive performance of the model, or produce explanations that are not transparent, trustworthy or do not capture the diversity of the data. To address these shortcomings, we propose ProtoVAE, a variational autoencoder-based ... -
RELAX: Representation Learning Explainability
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-11)Despite the significant improvements that self-supervised representation learning has led to when learning from unlabeled data, no methods have been developed that explain what influences the learned representation. We address this need through our proposed approach, RELAX, which is the first approach for attribution-based explanations of representations. Our approach can also model the uncertainty ... -
Selective Imputation for Multivariate Time Series Datasets with Missing Values
(Journal article; Tidsskriftartikkel, 2023-01-31)Multivariate time series often contain missing values for reasons such as failures in data collection mechanisms. Since these missing values can complicate the analysis of time series data, imputation techniques are typically used to deal with this issue. However, the quality of the imputation directly affects the performance of downstream tasks. In this paper, we propose a selective imputation ... -
Supercm: Revisiting Clustering for Semi-Supervised Learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-05)The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to obtain the desired results. In this work, we instead propose a novel approach that explicitly incorporates the underlying clustering assumption in SSL through extending ...
