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Holding Global Warming Deniers Accountable

Like it or not, human-enhanced global warming is an established scientific fact. Indeed, as much...

The Terrascope - Using Earth As A Lens

Ever since telescopes were first invented, by some dutch lens grinder in the late XVIth century...

My Take On The Breakthrough Prizes

I'll admit, I wanted to rather title this post "Billionaire Awards Prizes To Failed Theories",...

XENON Reports New Strong Limits On Light Dark Matter

Our current understanding of the Universe includes the rather unsettling notion that most of its...

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Tommaso DorigoRSS Feed of this column.

Tommaso Dorigo is an experimental particle physicist, who works for the INFN at the University of Padova, and collaborates with the CMS experiment at the CERN LHC. He coordinates the European network... Read More »

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The European Commission pays close attention to document the work of the projects that benefited of its funding. With that intent, the AMVA4NewPhysics network has been described, along with its goals, in a 2016 article on the Horizon magazine.

It is a bit embarrassing to post here a graph of boring elementary particle signals, when the rest of the blogosphere is buzzing after the release of the first real black hole image from the Event Horizon collaboration. So okay, before going into pentaquarks, below is the image of the black hole at the center of M87, a big elliptical galaxy 54 million light years away.


Yes, this is supposedly a particle physics blog, not a machine learning one - and yet, I have been finding myself blogging a lot more about machine learning than particle physics as of late. Why is that? 
Well, of course the topic of algorithms that may dramatically improve our statistical inference from collider data is of course dear to my heart, and has been so since at least two decades (my first invention, the "inverse bagging" algorithm, is dated 1992, when nobody even knew what bagging was). But the more incidental reason is that now _everybody_ is interested in the topic, and that means all of my particle physics and astroparticle physics colleagues. 
For the tenth anniversary of this blog being hosted by Science 2.0, which is coming in a few days, I decided to reinstall the habit I once had of weekly picking and commenting on a result from high-energy physics research, a series I called "The Plot Of The Week". These days I am busier than I used to be when this blog started being published here, so I am not sure I will be able to keep a weekly pace for this series; on the other hand I want to make an attempt, and the first step in that direction is this article.
Last Monday and Tuesday I gave a few lectures on Machine Learning at a Data Science school (IDPASC) in Braga, Portugal. I think that this topic has received so much attention in the last few years, with heaps of excellent resources now freely available online, that it is very difficult to be original and provide useful information to any student who is proactive enough to google "auto-encoders" by herself.


Update: a reader points out that a similar idea was already proposed and implemented in a commercial program. I'm glad to know this! (I would certainly not try to push my own implementation against a commercial product). I was however disappointed to see that the implementation, while perfectly acceptable from the point of view of quantum mechanics, is lacking in a few important ways from the chess logic point of view (some comments are in the thread below). Anyway, this is an example of a good idea coming too late...