Author Archives: rojefferson

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 observer-dependent 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

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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 SARS-CoV-2 pandemic, but … Continue reading

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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

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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) [1-7]. This connection was originally made in the context of certain lattice models, where decimation RG bears a superficial resemblance … Continue reading

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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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Black hole interiors, state dependence, and all that

In the context of firewalls, the crux of the paradox boils down to whether black holes have smooth horizons (as required by the equivalence principle). It turns out that this is intimately related to the question of how the interior … Continue reading

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