al_th

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Nov 22

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Comment · 1 year ago

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

Comment · 1 year ago

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

Comment · 1 year ago

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 ?