ReadNIPS Name Change by Terrence Sejnowski, Marian Stewart Bartlett, Michael Mozer, Corinna Cortes, Isabelle Guyon, Neil D. Lawrence, Daniel D. Lee, Ulrike von Luxburg, Masashi Sugiyama, Max Welling(nips.cc)
As many of you know, there has been an ongoing discussion concerning the name of the Neural Information Processing Systems conference. The current acronym NIPS has unintended connotations that some members of the community find offensive.
Following several well-publicized incidents of insensitivity at past conferences, and our acknowledgement of other less-publicized incidents, we conducted community polls requesting alternative names, rating the existing and alternative names, and soliciting additional comments.
After extensive discussions, the NIPS Board has decided not to change the name of the conference for now. The poll itself did not yield a clear consensus on a name change or a well-regarded alternative name.
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL )
Perhaps you've heard about IBM's giant Watson computer, which dispenses ingredient advice and novel recipes. Jaan Altosaar, a PhD candidate at Princeton University, is working on a recipe recommendation engine that anyone can use.<br><br>
<audio class="u-audio" src="http://media.blubrry.com/eatthispodcast/p/mange-tout.s3.amazonaws.com/2017/food2vec.mp3" preload="none" controls="controls"><a href="http://media.blubrry.com/eatthispodcast/p/mange-tout.s3.amazonaws.com/2017/food2vec.mp3">audio</a></audio><br>
<a class="button" title="download Eat This Podcast: A computer learns about ingredients and recipes" href="http://media.blubrry.com/eatthispodcast/p/mange-tout.s3.amazonaws.com/2017/food2vec.mp3">download</a><br><br>
Subscribe: <a class="powerpress_link_subscribe powerpress_link_subscribe_itunes" title="Subscribe on iTunes" href="//www.eatthispodcast.com/feed/podcast/" rel="nofollow">iTunes</a> | <a class="powerpress_link_subscribe powerpress_link_subscribe_android" title="Subscribe on Android" href="http://subscribeonandroid.com/www.eatthispodcast.com/feed/podcast/" rel="nofollow">Android</a> | <a class="powerpress_link_subscribe powerpress_link_subscribe_rss" title="Subscribe via RSS" href="http://www.eatthispodcast.com/feed/podcast/" rel="nofollow">RSS</a> | <a class="powerpress_link_subscribe powerpress_link_subscribe_more" title="More" href="http://www.eatthispodcast.com/how-to-subscribe/" rel="nofollow">More</a><br>
Support this podcast: <a href="https://www.patreon.com/etp">on Patreon</a>
Back in February I had retweeted something interesting from physicist and information theorist Michael Nielsen:
“Augmented cooking with machine intelligence”, with interesting remarks on generating food analogies… https://t.co/UluYk6p8TV
I found the article in it so interesting, there was some brief conversation around it and I thought to recommend it to my then new friend Jeremy Cherfas, whose Eat This Podcast I had just recently started to enjoy. Mostly I thought he would find it as interesting as I, though I hardly expected he’d turn it into a podcast episode. Though I’ve been plowing through back episodes in his catalog, fortunately this morning I ran out of downloaded episodes in the car so I started streaming the most recent one to find a lovely surprise: a podcast produced on a tip I made.
While he surely must have been producing the episode for some time before I started supporting the podcast on Patreon last week, I must say that having an episode made from one of my tips is the best backer thank you I’ve ever received from a crowd funded project.
Needless to say, I obviously found the subject fascinating. In part it did remind me of a section of Herve This’ book The Science of the Oven (eventually I’ll get around to posting a review with more thoughts) and some of his prior research which I was apparently reading on Christmas Day this past year. On page 118 of the text This discusses the classic French sauces of Escoffier’s students Louis Saulnier and Theodore Gringoire  and that a physical chemical analysis of them shows there to be only twenty-three kinds. He continues on:
A system that I introduced during the European Conference on Colloids and Interfaces in 2002  offers a new classification, based on the physical chemical structure of the sauce. In it, G indicates a gas, E an aqueous solution, H a fat in the liquid state, and S a solid. These “phases” can be dispersed (symbol /), mixed (symbol +), superimposed (symbol θ), included (symbol @). Thus, veal stock is a solution, which is designated E. Bound veal stock, composed of starch granules swelled by the water they have absorbed, dispersed in an aqueous solution, is thus described by the formula (E/S)/E.
This goes on to describe in a bit more detail how the scientist-cook could then create a vector space of all combinations of foods from a physical state perspective. A classification system like this could be expanded and bolted on top of the database created by Jaan Altosaar and improved to provide even more actual realistic recipes of the type discussed in the podcast. The combinatorics of the problem are incredibly large, but my guess is that the constraints on the space of possible solutions is brought down incredibly in actual practice. It’s somewhat like the huge numbers of combinations the A, C, T, and Gs in our DNA that could be imagined, yet only an incredibly much smaller subset of that larger set could be found in a living human being.
The additional byproduct of catching this episode was that it finally reminded me why I had thought the name Jaan Altosaar was so familiar to me when I read his article. It turns out I know Jaan and some of his previous work. Sometime back in 2014 I had corresponded with him regarding his fantastic science news site Useful Science which was just then starting. While I was digging up the connection I realized that my old friend Sol Golomb had also referenced Jaan to me via Mark Wilde for some papers he suggested I read.
T. Gringoire and L. Saulnier, Le répertoire de la cuisine. Dupont et Malgat, 1914.