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Author Archives: rojefferson
Islands behind the horizon
If you’ve been following the arXiv, or keeping abreast of developments in highenergy theory more broadly, you may have noticed that the longstanding black hole information paradox seems to have entered a new phase, instigated by a pair of papers … Continue reading
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QFT in curved space, part 2: Bogolyubov transformations & the Unruh effect
One of the most important lessons of QFT in curved space is that the notion of a particle is an observerdependent concept. That’s not to say it isn’t locally useful, but without specifying the details of the mode decomposition and … Continue reading
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QFT in curved space, part 1: Green functions
I was recently** asked to give a lecture on black hole thermodynamics and the associated quantum puzzles, which provided a perfect excuse to spend some time reviewing one of my favourite subjects: quantum field theory (QFT) in curved spacetime. I’ll … Continue reading
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Criticality in deep neural nets
In the previous post, we introduced mean field theory (MFT) as a means of approximating the partition function for interacting systems. In particular, we used this to determine the critical point at which the system undergoes a phase transition, and … Continue reading
Posted in Minds & Machines
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Mean field theory: from physics to deep neural nets
In a previous post, I alluded to the question of whether criticality played any role in deep neural networks. The question I originally had in mind was whether the fact that the correlation length diverges at a critical point implies … Continue reading
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Black hole thermodynamics, quantum puzzles, and the holographic principle
I was asked to give a lecture on “quantum puzzles and black holes” at the 20th Jürgen Ehlers Spring School, which was to be hosted at AEI this week. Unfortunately the school was cancelled due to the SARSCoV2 pandemic, but … Continue reading
Interior operators in AdS/CFT
In a previous post, I mentioned that the firewall paradox could be phrased as a question about the existence of interior operators that satisfy the correct thermal correlation functions, namely where and operators inside and outside the black hole, respectively; cf. … Continue reading
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Free energy, variational inference, and the brain
In several recent posts, I explored various ideas that lie at the interface of physics, information theory, and machine learning: We’ve seen, à la Jaynes, how the concepts of entropy in statistical thermodynamics and information theory are unified, perhaps the … Continue reading
Posted in Minds & Machines
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Deep learning and the renormalization group
In recent years, a number of works have pointed to similarities between deep learning (DL) and the renormalization group (RG) [17]. This connection was originally made in the context of certain lattice models, where decimation RG bears a superficial resemblance … Continue reading
Posted in Minds & Machines, Physics
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Cumulants, correlators, and connectivity
Lately, I’ve been spending a lot of time exploring the surprisingly rich mathematics at the intersection of physics, information theory, and machine learning. Among other things, this has led me to a new appreciation of cumulants. At face value, these … Continue reading
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