We present 27 problems encountered in automating the translation of movie/TV show subtitles.
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology.
We show that $L_2$ regularization leads to a simpler hypothesis class and better generalization followed by DARC1 regularizer, both for shallow as well as deeper architectures.
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets.