Right now DeepStack is using masked huber loss to compute the loss where the bucket is given weight 0 if impossible and 1 if possible.
What if we changed the mask so it can be any value between 0 and 1 weighted by how likely that bucket is?
So if there are 2 buckets A and B that both have error of 0.5, but bucket A has range probability 0.01, and bucket B has probability 0.0001, it would give 100x more importance to updating bucket A's CFV to become closer to its target.
Right now DeepStack is using masked huber loss to compute the loss where the bucket is given weight 0 if impossible and 1 if possible. What if we changed the mask so it can be any value between 0 and 1 weighted by how likely that bucket is?
So if there are 2 buckets A and B that both have error of 0.5, but bucket A has range probability 0.01, and bucket B has probability 0.0001, it would give 100x more importance to updating bucket A's CFV to become closer to its target.