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Interesting, Thanks a lot

unfortunate placement of speaker videos

ahhhhh ummm, AHHHHHH UMMMMM, AHH, UMMMM jesus christ

I am curious about the paper he references at 29:30 which gives bounds for linear regression case. I would appreciate it, if someone can share

This video is 2 years old. Do you have any more recent videos now in light of the recent almost 2,000,000 Sq/Km less sea ice than 2022 levels in Antarctic?

awesome Dr Felici

uhm thanks

All right. Got it. get the gold thing. Get the other thing painted in the other stuff so I can hold other stuff at a different temperature. Laser beams. Oh crap . nevermind . then the math came in. Oh God and you have to graph it out? I'm out of here

An eye-openning project to ML and its application! Very impressive.

Thank you for such a helpful video! Did my dissertation on machine learning in jet tagging and this was a really helpful introduction when I was getting started :)

Amazing talk.

Time Stamps 1:37 Large Hadron Collider setup LHC is located between border of Switzerland & France. Several experiments at the Large Hadron Collider (LHC) use detectors to analyse the myriad of particles produced by collisions in the accelerator: ATLAS, CMS, ALICE. ( My favourite experiemnent is Alpha which studies anti-matter system an anti-hydrogen but for now back to CMS & ATLAS) 2:03 CMS Detector explanation (Transverse slice of CMS showing various components/ sub-systems of the detector and their functions) 3:00 Particle flow algorithm to reconstruct full event. 3:48 Why Jets? 5:13 What kind of particles initiate Jet Tagging? Jet Tagging as a classification problem in machine learning 3 types of tagging - Jet flavour tagging: Distinguish Jets from bottom quarks, charm quarks or light flavoured quarks or gluon jets. - Tagging Hadronic decay of Tao leptons. - Boosted Jets: Particles are produced with very high momentum and high Lorentz boost particles end up coming very close to each other. In Boosted Jet Tagging we are trying to identify decay path such as t-->Wq-->qqq or h/W/Z-->qq and reject as background jets from single quark or gluon which are ubiquitously produced during interactions. 7:26 Main focus is on boosted jet tagging. Example Hadronic decay of highly Lorentz-boosted heavy particles (Higgs/W/Z/top) lead to large- radius jets with distinctive characteristics: Different radiation pattern (“substructure”) such as 3-prong(top), 2- prong(W/Z/H) or 1-prong(gluon/light flavour). 9:33 Jet Representation for Deep Learning: Image Approach 1: Convert Jets to 2D/3D Images and use Computer Vision. Approach 2: Convert to Sequence and use Natural Language Processing. Use Recurrent Neural Network , e.g. GRU/LSTM or 1D CNN. 11:22 DeepAk8 Architecture : Advance deep learning architecture for boosted jet tagging. 14:48 ParticleNet :Point Cloud Representation of a Jet Treat Jet as unordered set of particles.

I dig this guy. I got here from a channeled called “are we alone”

Can this be used to predict depth from UAV cameras? Or is it too focussed on cars

Love your teams work Max Tegmark! Funny timing with your mention of DeepMind AlphaFold at the beginning of this video and just a month later version 2 is announced that crushed version 1.

Very interesting topic

It is engaging, compare with SCIENCE AND TECHNOLOGY XXI: New Physica, Physics X.0 & Technology X.0 www.litres.ru/azamat-abdoullaev/science-and-technology-xxi-physics-x-0-technology-x-0/chitat-onlayn/page-5/