[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Attention mechanism-based bidirectional long short-term memory for cycling activity recognition using smartphones
Bicycles are an ecofriendly mode of transportation, and cycling offers physical and mental
well-being. However, their increased use has resulted in frequent bicycle–human accidents …
well-being. However, their increased use has resulted in frequent bicycle–human accidents …
A survey of challenges and opportunities in sensing and analytics for risk factors of cardiovascular disorders
Cardiovascular disorders cause nearly one in three deaths in the United States. Short-and
long-term care for these disorders is often determined in short-term settings. However, these …
long-term care for these disorders is often determined in short-term settings. However, these …
Clinical phenotyping with an outcomes-driven mixture of experts for patient matching and risk estimation
NC Hurley, SS Dhruva, NR Desai, JR Ross… - ACM Transactions on …, 2023 - dl.acm.org
Observational medical data present unique opportunities for analysis of medical outcomes
and treatment decision making. However, because these datasets do not contain the strict …
and treatment decision making. However, because these datasets do not contain the strict …
BayReL: Bayesian relational learning for multi-omics data integration
E Hajiramezanali, A Hasanzadeh… - Advances in …, 2020 - proceedings.neurips.cc
High-throughput molecular profiling technologies have produced high-dimensional multi-
omics data, enabling systematic understanding of living systems at the genome scale …
omics data, enabling systematic understanding of living systems at the genome scale …
Density-aware personalized training for risk prediction in imbalanced medical data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical
records, as most admitted patients survive. Training models with this imbalance rate (class …
records, as most admitted patients survive. Training models with this imbalance rate (class …
[PDF][PDF] Improving mc-dropout uncertainty estimates with calibration error-based optimization
A Shamsi, H Asgharnezhad, M Abdar… - arXiv preprint arXiv …, 2021 - academia.edu
Uncertainty quantification of machine learning and deep learning methods plays an
important role in enhancing trust to the obtained result. In recent years, a numerous number …
important role in enhancing trust to the obtained result. In recent years, a numerous number …
Sparse gated mixture-of-experts to separate and interpret patient heterogeneity in ehr data
A chalenge in developing machine learning models for patient risk prediction involves
addressing patient heterogeneity and interpreting the model outcome in clinical settings …
addressing patient heterogeneity and interpreting the model outcome in clinical settings …
Dynimp: Dynamic imputation for wearable sensing data through sensory and temporal relatedness
In wearable sensing applications, data is inevitable to be irregularly sampled or partially
missing, which pose challenges for any downstream application. An unique aspect of …
missing, which pose challenges for any downstream application. An unique aspect of …
An uncertainty-aware loss function for training neural networks with calibrated predictions
A Shamsi, H Asgharnezhad, AR Tajally… - arXiv preprint arXiv …, 2021 - arxiv.org
Uncertainty quantification of machine learning and deep learning methods plays an
important role in enhancing trust to the obtained result. In recent years, a numerous number …
important role in enhancing trust to the obtained result. In recent years, a numerous number …