I was a bit disappointed, even know there is no reason I should expect much in this space

- Tennis clip => ball is strongly unsynced with hit

- Dark mood beach video, no one in the screen => very high audio mood, lots of laughter like if it was summer on a busy beach

- Music inpainting completely switching style of audio (e.g. on the siren)

- "Electronic music with some buildup" : the gen just turns the volume up ?

I guess we have still some road to cover, but it feels like early image generation with out of touch hands and visual features. At least the generation are not non-sensical at all

This is entirely doable.

I'm absolutely not versed in RL, but I wanted to understand GRPO, the RL algorithm behind Deepseek's latest model.

I started from a very simple LLM, inspired from Andrej Karpathy's "GPT from scratch" video (https://www.youtube.com/watch?v=kCc8FmEb1nY). Then, I added onto that the GRPO algorithm, which in itself is very simple.

I made a GitHub repo if you want to try it out : https://github.com/Al-th/grpo_experiment

GRPO project is neat. Would you be willing to do a Karpathy-style explainer, breaking down the algorithm from scratch? It’s hard to understand on its own without prior background knowledge.

Find materials on PPO which should be widespread since it is the most popular RL algorithm. GRPO works on the same principles, just makes certain estimates from samples rather than training an auxiliary neural network to make them.

Interesting work.

Given, the recent noise around this paper https://arxiv.org/pdf/2407.07218 about "weak baselines" in ML x CFD work, I wonder how it resonates with this specific work..

I am not super familiar with DEM, but I know that other particle based model such as SPH benefit immensely from GPU acceleration. Does it make sense to compare with a CPU implementation ?

Besides, the output of the NeuralDEM seems to be rather coarse fields, correct ? In that sense, and again I'm not an expert of granular models so I might be entirely wrong, but does it make sense to compare with a method that is under a very different set of constraints ? Could we think about a numerical model that would allow to compute the same quantities in a much more efficient way, for example ?

Regarding your questions, yes, DEM also benefits a lot from GPU acceleration. So you can compare it to a CPU based code, but obviously there's an order of magnitude you can gain via GPU.

Usually you are not interested in the fine fields anyways. Think of some fine powder in a big process, where there are trillions of real particles inside. You can't and don't want to simulate that. Mostly you are interested in these course quantities anyways and getting statistical data, so for that there's no need for the fine resolution.

Regarding the numerical model that can compute these things in a more efficient way, they don't always exist. When you move to large numbers of particles you can sometimes go to continuum models, but they might not always behave as the real thing, as it's really difficult to find governing equations for such materials.

I haven't heard of this paper, very interesting read! Thank you for bringing it up here. Resonates very well with the (little) experience I have from playing around with CNN-based surrogate models years ago.