Is there a way to calculate cosine similarity between all combinations of embeddings?

[royboy (Roy Li)]

There’s no way to do it directly, but here’s a relatively low effort way to do this (just use cosine similarity instead of distance):

How to write custom loss function in terms of pairwise distances of outputs?

Hi there, I am trying to implement a loss function that depends on pairwise distances of output data. Specifically I have neural network f which take X as input and the output is $Y = {y_i}_{i=1}^N$. My error function has following form: $E = | d_{ij} - c_{ij} | ^2$ where d_{ij} is distance between y_i and y_j, and c_{ij} is the target value for this distance. I know how to write “vectorized” loss function like MSE, softmax which would take a complete vector to compute the loss. But in my …