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vineetm / ell-881-2018-deep-learning

Course Materials for ELL 881 2018: Fundamentals of Deep Learning
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Compatibility score #49

Open ankursharma-iitd opened 5 years ago

ankursharma-iitd commented 5 years ago

The other compatibility measure used in the paper involves taking the dot product of a vector u with the addition of L_i and G. I'm assuming that the weights of this vector u have to be learned during the training process. How can we implement this in our project? Can we simply map the addition result (L_i + G) to a scalar by passing it from perceptron (without non-linearity)?

sakshi18agrawal1 commented 5 years ago

We can use the second method mentioned in the paper. Compatibility score can be calculated directly by changing the dimensionality of L_i to that of G and then taking dot product of L_i and G.

VDnarendra commented 5 years ago

We can use the second method mentioned in the paper. Compatibility score can be calculated directly by changing the dimensionality of L_i to that of G and then taking dot product of L_i and G.

I also did in the same way.

ankursharma-iitd commented 5 years ago

Actually, we have to use both the methods for replicating the tables 1 and 2. (dp and cp) Anyway, adding a single-neuron layer works in my case for the cp part.