Export Reviews, Discussions, Author Feedback and Meta-Reviews

The authors compare results of shallow networks versus deep networks on the task at hand and demonstrate better performance for deep networks. They also use the scores of the shallow versus deep networks to analyse the role of basic vs. handcrafted features. GENERAL OPINION: This is a very straightforward paper that applies an existing technique on a novel, relevant and interesting application. The technical quality and clarity are very sound. Even though there is no real novelty on the side of machine learning research itself, in my opinion the introduction of a new application domain for deep learning in an extremely relevant scientific field in itself warrants acceptance. One caveat is that this reviewer has a background in physics, and is hence quite familiar with (and enthusiastic about) the concepts explained in the introduction. Whereas I found this explanation very clear, I cannot really speak for the rest of the NIPS community. DETAILED COMMENTS: - If possible, it might be informative to provide a depiction that illustrates the raw data one gets from a typical collision event (an illustration of the detected particles emerging from a single collision). This might give a better feel of the kind of data that is dealt with.