Hi, your work is very interesting and inspiring. However, I had some difficulties when I tried to repeat your experiments.
When I try your first code in run.sh (I just changed the dimension to 100 and keep all other arguments), it raises the error of tensor size mismatching. In fact, it occurs when i in some tasks. (I gauss there are some problems in the CenterIntersection class.)
In your paper, the part of the intersection seems to be non-parameterized, I wonder what the role of the two networks (GaussianOffsetIntersection and CenterIntersection ) on the intersection is in your model.
I gauss self.rank is the 'r' mentioned in Implementation Details in your paper. But why is the dimension of the center also multiplied by the self.rank? (For example, when I set the dimension to 100, the relation_embedding and offset_embedding are both 1600d. 16 is the self.rank.)
It seems that your memory L (size: d * r) instead of S=L*L^T as the inverse of the covariance matrix. However, I find you implement S_1+S_2 by L_1+L_2, but L_1L_1^T + L_2L_2^T is not the same as (L_1+L_2)(L_1+L_2)^T. (line 242-249 in models_gaussian.py)
I am sorry for asking you so many questions. Your work is so wonderful that I really look forward to hearing from you so that I can fully understand your work.
Hi, your work is very interesting and inspiring. However, I had some difficulties when I tried to repeat your experiments.
run.sh
(I just changed the dimension to 100 and keep all other arguments), it raises the error of tensor size mismatching. In fact, it occurs wheni
in some tasks. (I gauss there are some problems in theCenterIntersection
class.)GaussianOffsetIntersection
andCenterIntersection
) on the intersection is in your model.self.rank
is the 'r' mentioned in Implementation Details in your paper. But why is the dimension of the center also multiplied by theself.rank
? (For example, when I set the dimension to 100, therelation_embedding
andoffset_embedding
are both 1600d. 16 is theself.rank
.)models_gaussian.py
)I am sorry for asking you so many questions. Your work is so wonderful that I really look forward to hearing from you so that I can fully understand your work.