Some novel DL workloads in 2021

Table of Contents

Most Deep Learning Systems/Compilers in 2021 are still trying to optimize workloads like ResNet & VGG, though these models are really useful and we can publish papers on decent conference by proposing new compiler techniques for them (e.g. TASO/PET). I'm not so enthusiastic about competing on this track, Graph Neural Networks brings structured data into Deep Learning, which is good, but unfortunately the application of large scale GNNs are mostly limited to recommendation systems. I always expect my work to have broader positive social impact and apparently recommendation system can't.

I started surveying the evolving Deep Learning workloads this semester and try to find the "future" of Machine Learning Systems. Currently I feel like AlphaFold, Diffusion Model and Neural Radiance Field as three models that worth investigating into.

AlphaFold

TODO Multiple Sequence Alignment

TODO AlphaFold

TODO SE(3)-Transformer

Tensor Field Networks

\[ \textbf{f}(\textbf{x}) = \sum_{j=1}^{N} \textbf{f}_j \delta(\textbf{x} - \textbf{x}_j)\]

NVIDIA's optimization of SE(3)-Transformer

NVIDIA released an acceleration of SE(3)-Transformer.

RoseTTAFold

Diffusion Model

Lillian Weng's blog post is a good start point to know about diffusion models.

Neural Radiance Field (NeRF)

TODO Volume Rendering

TODO NeRF

Author: expye(Zihao Ye)

Email: expye@outlook.com

Date: 2021-10-28 Thu 00:00

Last modified: 2022-12-27 Tue 07:18

Licensed under CC BY-NC 4.0