1 code implementation • 30 Mar 2024 • Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman
We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits.
1 code implementation • 26 Jan 2024 • Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, James Hensman
Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Maximilian L. Croci, Ushnish Sengupta, Matthew P Juniper
The ensemble learns a surrogate of the approximate Bayesian posterior of the parameters given the observations, from which the flame can be re-simulated beyond the observation window of the experiment.
1 code implementation • 26 Apr 2021 • Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper
Heteroscedastic Bayesian neural network ensembles are trained on a library of 1. 7 million flame fronts simulated in LSGEN2D, a G-equation solver, to learn the Bayesian posterior distribution of the model parameters given observations.
no code implementations • 11 Oct 2020 • Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
The trained neural networks are then used to infer model parameters from real videos of a premixed Bunsen flame captured using a high-speed camera in our lab.