Teacher:In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
Teacher: Now, understand the problem? Solve this instance: 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?
Student:
en