![]() Figure 2: Distribution of topics across all submitted papers (Source: The review process for NIPS 2016) Unsurprisingly, Deep Learning (DL) was by far the most popular research topic, with about every fourth of more than 2,500 submitted papers (and 568 accepted papers) dealing with deep neural networks. Figure 1: The growth of the number of attendees at NIPS follows (the newly coined) Terry’s Law (named after Terrence Sejnowski, the president of the NIPS foundation faster growth than Moore's Law Law) The number of attendees skyrocketed at this year’s conference growing by over 50% year-over-year. One of the most accurate barometers for this evolution is the growth of NIPS itself. However, it is still sometimes hard to keep track of the actual extent of this development. Machine Learning seems to become more pervasive every month. ![]() This year, in slight juxtaposition, it took place in sunny Barcelona. It took place for the first time in 1987 and is held every December, historically in close proximity to a ski resort. The Conference on Neural Information Processing Systems (NIPS) is (besides ICML) one of the two top conferences in machine learning. In the following, I will share some of my highlights. The full conference program is available here. I attended NIPS 2016 in Barcelona from Monday, December 5 to Saturday, December 10. ![]() This post originally appeared at the AYLIEN blog. This post discusses highlights of the 2016 Conference on Neural Information Processing Systems (NIPS 2016).
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