Yes, that sounds great. I've put some talk details below.

Cheers,
David
Title: Accurate Deep Learning at Scale
Speaker: David Keys 

*Abstract: *A key barrier to the wide deployment of highly-accurate machine
learning models, whether for language or vision, is their high
computational and memory overhead. Although we possess the mathematical
tools for highly-accurate compression of such models, these
theoretically-elegant techniques require second-order information of the
model’s loss function, which is hard to even approximate efficiently at the
scale of billion-parameter models.In this talk, I will describe our work on
bridging this computational divide, which enables the accurate second-order
pruning and quantization of models at truly massive scale. Compressed using
our techniques, models with billions and even trillions of parameters can
be executed efficiently on a few GPUs, with significant speedups, and
negligible accuracy loss.

