Publications by authors named "Junier Oliva"
A comprehensive, collective approach to navigating the challenges of bias, privacy, and ethical considerations presented by the use of artificial intelligence in health care will require robust frameworks, continuous learning, and a commitment to equity. The insights and discussions presented in this issue are a testament to the ongoing efforts in North Carolina and beyond to find a balance between innovation with responsibility, ensuring that AI can deliver on its promise to enhance outcomes.
View Article and Find Full Text PDFWe propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e.
View Article and Find Full Text PDFArticle Synopsis
- Deep Learning (DL) methods are increasingly used for supervised learning but face challenges with missing data in datasets.
- The authors introduce a novel DL architecture that can handle both ignorable and non-ignorable missing data during training, specifically addressing missing not at random (MNAR) situations.
- Their approach is validated through simulations and a case study on the Bank Marketing dataset, showing that it outperforms existing methods in predicting client subscriptions based on incomplete phone survey data.