Congratulations. You are now officially Danish.

Thanks to my collaborators @rgring.bsky.social @lanalpa.bsky.social.

Try it out for yourself: github.com/DTU-PAS/spik...

We also get a much smaller input sizes with up to a 6.9x reduction over voxels and up to a 8.9x reduction over frames.

Results are pretty good. Inference speedups are up to 3.4x over voxels for a point cloud network and up to 10.4x over frames for a Transformer.

This comes without sacrificing accuracy. We even outperform voxels and frames in most cases on gesture recognition and object detection.

Spiking Patches works by creating a grid of patches and let each patch act as spiking neuron. A patch increases its potential whenever an event arrives within the patch, and a token is created everytime a patch spikes (when the potential exceeds a threshold).

We achieve this through tokenization of events. Our tokenizer is called Spiking Patches.

Something cool is that tokens are compatible with GNNs, PCNs, and Transformers.

This is the first time that anyone applies tokenization to events. We hope to encourage more of this.

How is external links to be understood? Is it e.g. okay to link to a video (not our own) with examples of a concept that we describe as a preliminary?

True. Not much of an issue on small codebases. Mostly just feels better with a snappier formatter for those.

black is great, but I prefer Ruff because of speed and it is also a really nice linter. docs.astral.sh/ruff/

Maybe this is it? arxiv.org/abs/2409.10202 @jakubgregorek.bsky.social