Predictive simulations are essential for applications ranging from weather forecasting to material design. The veracity of these simulations hinges on their capacity to capture the …
While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for …
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG) …
Predictive uncertainty--a model's self-awareness regarding its accuracy on an input--is key for both building robust models via training interventions and for test-time applications such …
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …
Short-term wind power forecasting has become a de facto tool to better facilitate the integration of such renewable energy resources into modern power grids. Instead of point …
Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However …
M Thiam, A Nakhaee - Geoenergy Science and Engineering, 2023 - Elsevier
Over the past decade, various artificial intelligence types have made significant progress in petroleum reservoir modeling, from machine learning to deep learning. These data-driven …
JH Oh, A Oldani, A Solecki, T Lee - Fuel, 2024 - Elsevier
Abstract Machine/deep learning (DL) predictions of sustainable aviation fuel's (SAF) physiochemical properties from chemical data offers a rapid way to prescreen the potential …