Task: Language Identification
Input (Spanish, English): Studying the Hessian in deep learning, the experiments in this paper suggest that the eigenvalue distribution is concentrated around zero and the non zero eigenvalues are related to the complexity of the input data. I find most of the discussions and experiments to be interesting and insightful. However, the current paper could be significantly improved.  Quality: It seems that the arguments in the paper could be enhanced by more effort and more comprehensive experiments. Performing some of the experiments discussed in the conclusion could certainly help a lot. Some other suggestions: 1- It would be very helpful to add other plots showing the distribution of eigenvalues for some other machine learning method for the purpose of comparison to deep learning. 2- There are some issues about the scaling of the weights and it make sense to normalize the weights each time before calculating the Hessian otherwise the result might be misleading. 3- It might worth trying to find a quantity that measures the singularity of Hessian because it is difficult to visually conclude something from the plots. 4- Adding some plots for the Hessian during the optimization is definitely needed because we mostly care about the Hessian during the optimization not after the convergence.  Clarity: 1- There is no reference to figures in the main text which makes it confusing for the reading to know the context for each figure. For example, when looking at Figure 1, it is not clear that the Hessian is calculated at the beginning of optimization or after convergence. 2- The texts in the figures are very small and hard to read.
Output (English): en