Task: Language Identification
Input (Spanish, English): The paper propose to find an optimal decoder for binary data using a min-max decoder on the binary hypercube given a linear constraint on the correlation between the encoder and the  data.  The paper gives finally that the optimal decoder as logistic of the lagragian W multiplying the encoding e.   Given the weights of the ‘min-max’decoder W the paper finds the best encoding for the data distribution considered, by minimizing that error as a function of the encoding.  The paper then alternates that optimization between the encoding and the min-max decoding, starting from random weights W.   clarity:  -The paper would be easier to follow if the real data (x in section 3 ) is differentiated from the worst case data played by the model (x in section 2).    significance  Overall I like the paper, however I have some doubts on what the alternating optimization optimum ends up being.  The paper ends up implementing a single layer network. The correlation constraints while convenient in the derivation, is  a bit intriguing. Since linear relation between the encoding and the data  seems to be weak modeling constraint and might be not different from what PCA would implement.  - what is the performance of PCA on those tasks? one could you use a simple sign function to decode. This is related to one bit compressive sensing.  - what happens if you initialize W in algorithm one with PCA weights? or weighted pca weights?  - Have you tried on more complex datasets such as cifar?
Output (English): en