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AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry
In the insurance industry detecting fraudulent claims is a critical task with a significant financial impact.
GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
In this work, we propose a multimodal framework (GaLeNet) for assessing the severity of damage by complementing pre-disaster images with weather data and the trajectory of the hurricane.
On the Trustworthiness of Tree Ensemble Explainability Methods
The recent increase in the deployment of machine learning models in critical domains such as healthcare, criminal justice, and finance has highlighted the need for trustworthy methods that can explain these models to stakeholders.
Prediction of Workplace Injuries
In this study, we investigate the problem of injury risk prediction and prevention in a work environment.
Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia
We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face.
A Hybrid Instance-based Transfer Learning Method
Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target).
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning
We propose a subspace transfer learning method, in which we select a subspace from the source that best describes the target space.
