I don’t think an “actual distribution” over the activations is a thing? The distribution depends on what inputs you feed it.
This seems to be what Thomas is saying as well, no?
[...] look at the network activations at each layer for a bunch of different inputs. This gives you a bunch of activations sampled from the distribution of activations. From there, you can do density estimation to estimate the actual distribution over the activations.
The same way you can talk about the actual training distribution underlying the samples in the training set it should be possible to talk about the actual distribution of the activations corresponding to a particular input distribution.
I believe Thomas is asking how you plan to do the first step of: Samples → Estimate underlying distribution → Get modularity score of estimated distribution
While from what you are describing I’m reading: Samples → Get estimate of modularity score
This seems to be what Thomas is saying as well, no?
The same way you can talk about the actual training distribution underlying the samples in the training set it should be possible to talk about the actual distribution of the activations corresponding to a particular input distribution.
I believe Thomas is asking how you plan to do the first step of: Samples → Estimate underlying distribution → Get modularity score of estimated distribution
While from what you are describing I’m reading: Samples → Get estimate of modularity score
Thanks for clarifying for me, see the edit in the parent comment.
Thanks, this is indeed what I was asking.