mpierini

7 ms·

1. 1.▲
   by mpierini 6mo ago

   A lot of data. More than google + netflix + you name it. And you have 100 nsec. And the data are not on disk. Good luck with your linear regression
2. 2.▲
   by mpierini 6mo ago

   You do unsupervised learning without labels with a linear regression. Interesting. What would you regress in this case? The problem is the following: you have a point cloud of data (electronic signal from arrays arranged into an irregular
3. 3.▲
   by mpierini 6mo ago

   Wrong! We do how to solve the problem, but the solution does not run on an electronic board at 100 nsec. So it has to be approximated with a function that runs within that time constraint. Also, if deciding to accept/reject events ba
4. 4.▲
   by mpierini 6mo ago

   Perplexity, aka measuring how much a network is sure about its answer. Which might be wrong. It would not pass the pier review of any particle physics journal. (Real) science is about being right, not about being sure about itself.
5. 5.▲
   by mpierini 6mo ago

   And this problem is a joke compared to a real problem. We are talking about going from 40 MHz to 100 kHz incoming data stream, after which a second layer of real-time selection reduces the data to 1 kHz which is processed, cleaned, elaborat
6. 6.▲
   by mpierini 6mo ago

   And you think we did not try linear regressions? This is what we used to do 20 years ago. Then we gained two orders of magnitude in signal-to-background discrimination. And since our data are not even images, off-shelf solutions mostly don’