FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333

Today we close out our 2019 NeurIPS series with Mohamed Sidahmed, Machine Learning and Artificial Intelligence R&D Manager at Shell. In our conversation, we discuss: 

The papers Mohamed and his team submitted to the conference this year, in particular: 
Accelerating Least Squares Imaging Using Deep Learning Techniques, which details how researchers can computationally efficiently reconstruct imaging using a deep learning framework approach.

FaciesNet: Machine Learning Applications for Facies Classification in Well Logs, which Mohamed describes as “A novel way of designing a new architecture for how we use sequence modeling and recurrent networks to be able to break out of the benchmark for classifying the different types of rock.” 

The full show notes for this episode can be found at twimlai.com/talk/333. Make sure you head over to twimlai.com/neurips2019 to follow along with this series!