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mberr avatar mberr commented on May 29, 2024

Besides the 1/2 term this should be MSELoss

pykeen/src/pykeen/losses.py

Lines 132 to 141 in ef83e7e

class MSELoss(PointwiseLoss):
"""A wrapper around the PyTorch mean square error loss."""
def forward(
self,
scores: torch.FloatTensor,
labels: torch.FloatTensor,
) -> torch.FloatTensor: # noqa: D102
assert self.validate_labels(labels=labels)
return functional.mse_loss(scores, labels, reduction=self.reduction)

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cthoyt avatar cthoyt commented on May 29, 2024

Oh whoops, I missed that. Why did we include the 1/2 in the paper but not in the implementation?

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mberr avatar mberr commented on May 29, 2024

Oh whoops, I missed that. Why did we include the 1/2 in the paper but not in the implementation?

Sorry for the belated answer; I somehow missed the notification.

The 1/2 is often added in front of squared loss in order to have a nicer derivative for analytical derivations (since it cancels the 2*... of the ...^2 derivative). For the location of the minimum is does not play a role (since argmin f(x) = argmin c*f(x) for c != 0).

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