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: This paper introduces a mechanism for active learning with convolutional neural networks (CNNs). I would not go as far as the authors in calling these "deep", seeing that they seem to have only 2 hidden layers with only 20 filters each. The active learning criterion is a greedy selection scheme based on variational free energy and a series of approximations. The paper is sometimes hard to read, due to (a) many grammatical errors and (b) sloppy notation in some places (e.g., on page 5, line 1, f is used but never introduced before). Overall, I give an accepting score, but a weak one because of the grammatical errors. If the paper is accepted, these should be fixed for the final version, optimally by a native speaker. The paper's topic is interesting, and the paper appears to succeed in its goal of showing a proof of concept for active learning in CNNs (if only on toy datasets). I'm surprised by the new results on uncertainty sampling and curriculum learning the authors added: why do these methods both break for USPS? In particular, uncertainty sampling did very well (in fact, better than the authors' new method) on MNIST, but apparently horribly on USPS; some explanation for this would be useful. I have one more question: why is it necessary to first sample a larger subset D \subset U, from which we select using active learning? Is this merely done for reasons of computational efficiency, or can it actually somehow improve results? (If so, it would be instrumental to see the worse results when this is not done.)
Student:
en