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Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations

Post-hoc explanation methods provide interpretation by attributing predictions to input features. ...

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ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but cl...

A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification

In many medical imaging tasks, convolutional neural networks (CNNs) efficiently extract local feat...

Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI

This study addresses the challenge of predicting post-stroke rigidity by emphasizing feature inter...

Concept Extraction for Time Series with ECLAD-ts

Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly u...

Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation

Deep learning has been successfully applied to medical image segmentation, enabling accurate ident...

Opening the Black-Box: Symbolic Regression with Kolmogorov-Arnold Networks for Energy Applications

While most modern machine learning methods offer speed and accuracy, few promise interpretability ...

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Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models

Accurate medical symptom coding from unstructured clinical text, such as vaccine safety reports, i...

BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models

Accurate segmentation of polyps and skin lesions is essential for diagnosing colorectal and skin c...

Timely Trajectory Reconstruction in Finite Buffer Remote Tracking Systems

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance...

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Sim-is-More: Randomizing HW-NAS with Synthetic Devices

Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa ...

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A Scalable Predictive Modelling Approach to Identifying Duplicate Adverse Event Reports for Drugs and Vaccines

The practice of pharmacovigilance relies on large databases of individual case safety reports to d...

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient manag...

Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation

Code translation migrates codebases across programming languages. Recently, large language models ...

Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety

Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilize...

PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction

Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resourc...

Improving Quantization with Post-Training Model Expansion

The size of a model has been a strong predictor of its quality, as well as its cost. As such, the ...

Preferential Multi-Objective Bayesian Optimization for Drug Discovery

Despite decades of advancements in automated ligand screening, large-scale drug discovery remains ...

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Post-composing ontology terms for efficient phenotyping in plant breeding.

Ontologies are widely used in databases to standardize data, improving data quality, integration, an...

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Mar 2025 40117331
Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients

Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed t...

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