相关论文: SHIP: A Shapelet-based Approach for Interpretable …
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome in observational data. Our approach tests the joint…
The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been…
Recent advancements in medical image analysis have predominantly relied on Convolutional Neural Networks (CNNs), achieving impressive performance in chest X-ray classification tasks, such as the 92% AUC reported by AutoThorax-Net and the…
The increasing capture and analysis of large-scale longitudinal health data offer opportunities to improve healthcare and advance medical understanding. However, a critical gap exists between (a) -- the observation of patterns and…
Vision-Language Models have demonstrated strong potential in medical image analysis and disease diagnosis. However, after deployment, their performance may deteriorate when the input data distribution shifts from that observed during…
In recent years, two parallel research trends have emerged in machine learning, yet their intersections remain largely unexplored. On one hand, there has been a significant increase in literature focused on Individual Treatment Effect (ITE)…
The identification of complex periodic windows in the two-dimensional parameter space of certain dynamical systems has recently attracted considerable interest. While for discrete systems, a discrimination between periodic and chaotic…
Situational awareness (SA) is essential for effective team performance in time-critical clinical environments, yet its dynamic and distributed nature remains difficult to characterize. In this preliminary study, we apply Transition Network…
Despite significant progress in intelligent fault diagnosis (IFD), the lack of interpretability remains a critical barrier to practical industrial applications, driving the growth of interpretability research in IFD. Post-hoc…
Interpretability in Vision-Language Models (VLMs) is crucial for trust, debugging, and decision-making in high-stakes applications. We introduce PixelSHAP, a model-agnostic framework extending Shapley-based analysis to structured visual…
In an era where maritime infrastructures are crucial, advanced situational awareness solutions are increasingly important. The use of optical camera systems can allow real-time usage of maritime footage. This thesis presents an…
The choice of lung protective ventilation settings for mechanical ventilation has a considerable impact on patient outcome, yet identifying optimal ventilatory settings for individual patients remains highly challenging due to the inherent…
Autonomous navigation in marine environments can be extremely challenging, especially in the presence of spatially varying flow disturbances and dynamic and static obstacles. In this work, we demonstrate that incorporating local flow field…
The maritime shipping industry is undergoing rapid evolution driven by advancements in computer vision artificial intelligence (AI). Consequently, research on AI-based object recognition models for maritime transportation is steadily…
Premature ventricular contraction(PVC) is a type of premature ectopic beat originating from the ventricles. Automatic method for accurate and robust detection of PVC is highly clinically desired.Currently, most of these methods are…
When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among…
Industrial Internet of Things environments increasingly rely on advanced Anomaly Detection and explanation techniques to rapidly detect and mitigate cyberincidents, thereby ensuring operational safety. The sequential nature of data…
Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these signals exhibit strong non-stationarity, with sharp peaks and…
Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical…
Vision-Language-Action (VLA) models have demonstrated strong potential for predicting semantic actions in navigation tasks, demonstrating the ability to reason over complex linguistic instructions and visual contexts. However, they are…