4 citations · 4 across the 4 of their papers we have counts for
4 papers
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
Alexander Mathiasen, Hatem Helal, Paul Balanca +6
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as . Schütt et al. (2019) successfully appro…
Generating QM1B with PySCF
Alexander Mathiasen, Hatem Helal, Kerstin Klaser +6
The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets w…
Training and inference of large language models using 8-bit floating point
Sergio P. Perez, Yan Zhang, James Briggs +6
FP8 formats are gaining popularity to boost the computational efficiency for training and inference of large deep learning models. Their main challenge is that a careful choice of…
Unit Scaling: Out-of-the-Box Low-Precision Training
Charlie Blake, Douglas Orr, Carlo Luschi
We present unit scaling, a paradigm for designing deep learning models that simplifies the use of low-precision number formats. Training in FP16 or the recently proposed FP8 format…