202 citations · 467 across the 38 of their papers we have counts for
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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…
Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model
Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 plumes is weak in amplitude and therefo…
Ultra-low memory seismic inversion with randomized trace estimation
Mathias Louboutin, Felix J. Herrmann
Inspired by recent work on extended image volumes that lays the ground for randomized probing of extremely large seismic wavefield matrices, we present a memory frugal and computat…
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin +2
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneou…