we stumble on a weird observation where the few features with the least sparsity are not even learned and represented in the hidden layer
I’m not sure how you’re modeling sparsity, but if these features are present in nearly 100% of inputs, you could think of it as the not-feature being extremely sparse. My guess is that these features are getting baked into the bias instead of the weights so the model is just always predicting them.
Hey, i’m controlling the sparsity when I’m creating the batch of the data, so during that time, i sample according to the probability i’m assigning for that feature.
re: features getting baked into the bias: yeah, that might be one of the intuitions we can develop but to me the interesting part is that that kind of behaviour didn’t happen in any of the other cases when the importance was varying and just happened when the feature importance for all of them is equal. I don’t have a concrete intuition on why that might be the case, still trying to think on it.
I’m not sure how you’re modeling sparsity, but if these features are present in nearly 100% of inputs, you could think of it as the not-feature being extremely sparse. My guess is that these features are getting baked into the bias instead of the weights so the model is just always predicting them.
Hey, i’m controlling the sparsity when I’m creating the batch of the data, so during that time, i sample according to the probability i’m assigning for that feature.
re: features getting baked into the bias: yeah, that might be one of the intuitions we can develop but to me the interesting part is that that kind of behaviour didn’t happen in any of the other cases when the importance was varying and just happened when the feature importance for all of them is equal. I don’t have a concrete intuition on why that might be the case, still trying to think on it.