Task: Language Identification
Input (Spanish, English): This is an 18 page paper plus appendix which presents a mathematical derivation for infomax for an actual neural population with noise.  The original Bell & Sejnowski infomax framework only considered the no noise case.  Results are shown for natural image patches and the mnist dataset, which qualitatively resemble results obtained with other methods.  This seems like an interesting and potentially more general approach to unsupervised learning.  However the paper is quite long and it was difficult for me to follow all the twists and turns.  For example the introduction of the hierarchical model was confusing and it took several iterations to understand where this was going.  'Hierarchical' is probably not the right terminology here because it's not like a deep net hierarchy, it's just decomposing the tuning curve function into different parts.  I would recommend that the authors try to condense the paper so that the central message and important steps are conveyed in short order, and then put the more complete mathematical development into a supplementary document.  Also, the authors should look at the work of Karklin & Simoncelli 2011 which is highly related.  They also use an infomax framework for a noisy neural population to derive on and off cells in the retina, and they show the conditions under which orientation selectivity emerges.
Output (English): en