3 papers
cs.LG2026
XConv: Low-memory stochastic backpropagation for convolutional layers
Anirudh Thatipelli, Jeffrey Sam, Mathias Louboutin +3
Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation. Existing remedies (checkpoi…
cs.DC2025
Automated MPI-X code generation for scalable finite-difference solvers
George Bisbas, Rhodri Nelson, Mathias Louboutin +3
Partial differential equations (PDEs) are crucial in modeling diverse phenomena across scientific disciplines, including seismic and medical imaging, computational fluid dynamics,…
cs.LG2024
Machine learning-enabled velocity model building with uncertainty quantification
Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng +2
Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration project…