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From the equation (2) in the paper, the second order term should be a scalar. However, in the code, it is computed as a vector. I think the line 102 should be something like this: self.y_second_order = 0.5 * tf.reduce_sum(tf.subtract(self.summed_features_emb_square, self.squared_sum_features_emb))
The text was updated successfully, but these errors were encountered:
IMO, the original paper said each of three components should all be one scalar, then sum together to fed to sigmod() . But you can also treat the sum of scalar as sum(w1x1, w2x2...), and set w1=w2=..=1。In this git repo, the author treat all W as trainable variables, which is an extension to original paper. Although effect of this modification is unknown, this implementation is still following the core ideas of the paper.
From the equation (2) in the paper, the second order term should be a scalar. However, in the code, it is computed as a vector. I think the line 102 should be something like this:
self.y_second_order = 0.5 * tf.reduce_sum(tf.subtract(self.summed_features_emb_square, self.squared_sum_features_emb))
The text was updated successfully, but these errors were encountered: