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].
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Question: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.

Answer: en


Question: This paper presents a system approach to combine multiple modalities to perform classification in a practical scenario (e-commerce).  In general, I find the proposed approach in the paper sound and solid, but do not see novelty in the paper: feature fusion and decision time fusion are both standard practices in multi-modal analysis, and the rest of the paper offers no surprise in implementing such approaches. This seems to be a better fit for venues that focus more on production systems, and seems to be a bad fit for ICLR where the focus is more on research of novel algorithms and theories.

Answer: en


Question: Paper Strengths:  -- Elegant use of MoE for expanding model capacity and enabling training large models necessary for exploiting  very large datasets in a computationally feasible manner  -- The effective batch size for training the MoE drastically increased also  -- Interesting experimental results on the effects of increasing the number of MoEs, which is expected.   Paper Weaknesses:  --- there are many different ways of increasing model capacity to enable the exploitation of very large datasets; it would be very nice to discuss  the use of MoE and other alternatives in terms of computational efficiency and other factors.

Answer:
en