Although JAX is faster than the others via jit compiling, another strong point of JAX is intuitive autograd.
This project will try to exploit an advantage of JAX autograd while reproducing Neural ODE and Neural SDE which is suitable for this purpose.

This project will use Imagenet and CIFAR-10 dataset.
The models to reproduce can be found in below papers,
– Neural ODE https://arxiv.org/pdf/1806.07366.pdf 24
– Score-Based Generative Modeling through Stochastic Differential Equations | OpenReview 24


Please feel free to suggest any opinion about this project 
Some of you might think this project requires mathematical understanding and harder than the others.
However, there are plenty of materials we can help you understand its math background such as

implicit-layers-tutorial at ICML20’
so do not hesitate 


Current team members:
sw32-seo
@wgchang
@lenscloth
Thanks,
Seung-woo Seo