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10 papers · 2 filters
Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach
Amir Rafe, Patrick A. Singleton
This study presents the first investigation of pedestrian crash severity using the TabNet model, a novel tabular deep learning method exceptionally suited for analyzing the tabular…
FLASC: A Flare-Sensitive Clustering Algorithm
D. M. Bot, J. Peeters, J. Liesenborgs +1
Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful…
Unveiling The Factors of Aesthetic Preferences with Explainable AI
Derya Soydaner, Johan Wagemans
The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspectiv…
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Kun Fang, Qinghua Tao, Xiaolin Huang +1
Existing Out-of-Distribution (OoD) detection methods address to detect OoD samples from In-Distribution (InD) data mainly by exploring differences in features, logits and gradients…
Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff
Freek Holvoet, Katrien Antonio, Roel Henckaerts
Insurers usually turn to generalized linear models for modeling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining pop…
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
Meng Shang, Lenore Dedeyne, Jolan Dupont +8
Otago Exercise Program (OEP) is a rehabilitation program for older adults to improve frailty, sarcopenia, and balance. Accurate monitoring of patient involvement in OEP is challeng…