Today was the second day of Machine Learning Tools for Research in Astronomy. Two personal highlights were the following:
Soledad Villar (NYU) spoke about adversarial attacks against machine-learning methods used in astrophysics. Her talk was almost entirely conceptual; she talked about what constitutes a successful attack, and how you find it. My expectation is that these attacks will be very successful, as my loyal reader knows! The examples she showed were from stellar spectroscopy. Her talk was interrupted and followed by extremely lively discussion, in which the room disagreed about what the attacks mean about a method, and whether they are revealing or important. That was some fun controversy for the meeting.
Tobias Buck (AIP) looked at methods to translate from image to image (like horse to zerbra!) but in the context of two-d maps of galaxies. Like translate from photometric images into maps of star formation and kinematics. It's a promising area, although the training data are all simulations at this point. I asked him whether he could translate from a two-d galaxy image into a three-d dark-matter map. He was skeptical, because the galaxy is so much smaller than its dark-matter halo.
At one of the coffee breaks, Josh Peek (STScI) proposed that we craft some kind of manifesto or document that helps practitioners in machine learning in astronomy make good choices, which are pragmatic (because it is important that machine learning be used and tried) but also involve due diligence (to avoid the “just throw machine learning at it” problem in some of the literature). He had the idea that we have the right people at this meeting to make something like this happen. I noted that we tried to do things like that in our tutorial paper on MCMC sampling, where we try to both be pragmatic but also recommend achievable best practices. The challenge is to be encouraging and supportive, but also draw some lines in the sand.
No comments:
Post a Comment