10 citations · 17 across the 5 of their papers we have counts for
5 papers · 1 filter
In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
Daniel O'Malley, Christopher W. Johnson, Javier E. Santos +10
Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential for geothermal resource assess…
The Transparent Earth: A Multimodal Foundation Model for the Earth's Subsurface
Arnab Mazumder, Javier E. Santos, Noah Hobbs +2
We present the Transparent Earth, a transformer-based architecture for reconstructing subsurface properties from heterogeneous datasets that vary in sparsity, resolution, and modal…
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach
Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey +2
Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of n…
A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting
Arnab Neelim Mazumder, Tinoosh Mohsenin
Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibi…
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
Morteza Hosseini, Haoran Ren, Hasib-Al Rashid +3
Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the…