3 ms·

I appreciate your work, but could you also include the size of the model in bytes and information about inference speed in your tables? Seeing a bunch of very s

I appreciate your work, but could you also include the size of the model in bytes and information about inference speed in your tables? Seeing a bunch of very similar numbers just tells us that MNIST is not a very challenging benchmark for Convolutional KANs. One of the key reasons one would want to reduce a model's size is so that the model can fit on very small low power accelerators such as corral TPUs. SRAM is a precious resource and needs to be conserved.

groby_b 2y ago
From the doc: > At the moment we aren't seeing a significant improvement in the performance of the KAN Convolutional Networks compared to the traditional Convolutional Networks. So, inference speed probably not improved. As for model size in bytes, is there any reason to assume it's not directly related to parameter count? I didn't see any mentions of pruning/quantization/other optimizations, so I'll naively go with KKAN being about 400KB. (For inference purposes) Either way, probably still a bit too early to think about productionizing KANs - there's still a ton of unanswered questions. The biggest one for now probably the fact that they are sloo-ooo-ooow to train.(10x slower than MLPs) On the upside, you can probably get quite a bit of traction if you publicly look at KANs on low power accelerators, it seems a very open topic :)