deepmind-research VS swav

Deepmind 63

Found some here and here.

I am trying to make a physics-simulation model based on DeepMind's research, with its source code found here https://github.com/deepmind/deepmind-research/tree/master/learning_to_simulate . The thing that mainly confuses me is how to properly implement the embedding situation found at https://github.com/deepmind/deepmind-research/blob/master/learning_to_simulate/learned_simulator.py on lines 78 and 152.

Yes. There's a fair bit of research into using ML to generate 3D models. Early work, like Neural Radiance Fields (NeRF) generated a voxel model, which could be used for 3D printing, but it would be low resolution, like blowing up a tiny image vs an SVG vector file. However, more recent research can generate polygonal models from a video taken of a real object. Polygonal models are much better for 3D printing.

Skilful precipitation nowcasting using deep generative models of radar https://www.nature.com/articles/s41586-021-03854-z https://deepmind.com/blog/article/nowcasting https://github.com/deepmind/deepmind-research/tree/master/nowcasting

Github: https://github.com/deepmind/deepmind-research/tree/master/density_functional_approximation_dm21

BYOL - again, it seems that it's not optimized for running on multiple GPUs.

SwAV works in multigpu but the official implementation isn't that great https://github.com/facebookresearch/swav

But if I take the github of swav, they do put:

Code for https://arxiv.org/abs/2006.09882 found: https://github.com/facebookresearch/swav