There are more complete versions of this kind of thing publicly available: https://github.com/turtlesoupy/this-word-does-not-exist

> This would be amazing, for example, to run on a large corpus, generate the dictionary, and then run it again to find words that are used but not defined - not just in the original corpus but in the definitions too.

I think this would be how you would gauge success of the model. That is to say, you would evaluate model accuracy on a set of held-out words with definitions that never appeared in your dictionary training set but appeared in context in your corpus. You would have to manually annotate whether or not the generated definition of these held out words was acceptable.

Imagine that you have a whole bunch generative models (its best if you imagine a fully connected Boltzmann machine in particular, whose states you can think of as a binary vector consisting only of zeros and ones) that have the same form but different random realizations of their parameters. This is a typical example of what a toy model of a so-called "spin glass" looks like in statistical physics (the spins are either up down down, usually represented as +1/-1). Each of these models, having been initialized randomly will have their up particular frequency of a particular location (also called site) of the boolean vector being either a one or a zero.

If the tendency of a site to be either or one or a zero was independent of every other site the analysis of such a model would be pretty straightforward: every model would just have a vector of N frequencies and we could compare how close the statistical behavior of each model was to the other by comparing how closely the N frequencies at each site matched one another. But in the general case there will be some interaction or correlation between sites in a given model. If the interaction strength is strong enough this can result in a model tending to generate groups of patterns in its sequence of zeros and ones that are close to one another. Furthermore if we compare the overlap of the apparent patterns between two such models, each with their own random parameters, we will find that some of them overlap more than others.

What we can then do is to ask the question of how much, on average do the patterns of these random models overlap with on another in the full set of all models. This leads us to the concept of an "overlap matrix". This matrix will have one set of values along the diagonal (corresponding to how much a models patterns tend to overlap with themselves) and off diagonal values capturing the overlap between. You can find through simulation or with some carefully constructed calculations that when the interaction strength between sites is small that the off diagonal elements don't tend to zero, but rather a single number different from the diagonal value. This is perhaps intuitive: these models were randomly initialized but they are going to overlap in their behavior in some places.

Where things get interesting though is when you increase the interaction strength you find that the overlap matrix starts to take on a block diagonal form, wherein clusters of models overlap with one another at a certain level and at a lower but constant level with out-of-cluster models. This is called one replica symmetry breaking (1RSB). These different clusters of models can be thought of as having learned different overall patterns with the similarity quantified by their overalp. If you keep increasing the interaction strength you will find that this happens again and again, with a k-fold replica symmetry braking (kRSB) with a sort of self similar block structure emerging in the overlap matrix (picture is worth a thousand words [1]).

Now the real wild part that Parisi figured out is what happens when you take this process to the regime of full replica symmetry breaking. You can't really do this with simulations and the calculations are very tricky (you have a bunch of terms either going to infinity or zero that need to balance out correctly) but Parisi ending up coming up with an expression for the distribution of overlaps for the infinitely sized matrix with full interaction strength in play. The expression is actually a partial differential equation that itself needs to be solved (I told you the calculations were tricky right), but amazingly, it seems to capture the behavior of these kinds of models correctly.

Whereas mathematicians have a pretty good idea of how to understand the 1RSB process rigorously, the Parisi full replica symmetry breaking scheme is very much not understood and remains of interest both to complex systems researches trying to understand their models and applied mathematicians (probability people in particular) trying to lay the foundations needed to explore the ideas being explored by theorists.

Hope that helps a bit!

[1] https://www.semanticscholar.org/paper/Spin-Glasses%2C-Boolea...

[1] https://neurophys.biomedicale.parisdescartes.fr/wp-content/u... [2] https://ml-jku.github.io/hopfield-layers/

Comments URL: https://news.ycombinator.com/item?id=25873006

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Not quite. It's really just that they require the dynamics to be Hamiltonian, which would be highly atypical of the kind of dynamics an otherwise unconstrained neural network would learn. This is reflected in their loss function, the first of which learn an arbitrary second order differential equation, the second of which enforces Hamiltonian dynamics.

I don't understand how this was considered novel enough to warrant at PRE paper.

Here is a link to the paper:

https://journals.aps.org/pre/pdf/10.1103/PhysRevE.101.062207

Agreed. But I think quantifications can still be useful.

Person-years probably aren't the right sort of quantification though, in that they blend together too many kinds of human experience. But I imagine most people would agree that statements like "X% of grandparents, lost 10 years earlier than expected" or "Y% of children under 10" allow for comparing the relative impact on families.

Quantifying losses doesn't mean that we consider the quantified as fungible.

Comments URL: https://news.ycombinator.com/item?id=22542596

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I might also say that a second major discriminating factor is skin texture, especially at boundaries.

Regarding the first caveat that you bring up though, whereas the problem statement says you need a Hermitian matrix I think the results should generalize to non-hermitian matrices. In particular take a look at the third proof of the theorem at the end of the article. The only assumption required here is that the eigenvalues are simple (which does not preclude them being complex/coming in complex pairs).

Protip: I had to read the second step in the proof a few times before I could see what was going on. Explicitly what you do here is to (i) multiple from the right by the elementary unit vector e_j (ii) set up the matrix vector inverse using Cramer's rule (iii) notice that this matrix can be permuted to have a block diagonal element equal to the minor M_i with the other block diagonal entry equal to 1 and the corresponding column equal filled with zeros (iv) Write the determinant in the block form, which simplifies to the expression involving M_i by itself then finally (v) multiply by e_j from the left to get scalar equation.

Both the context and secrete message change the encrypted message

However, you have written

> I think when I took physics it really brought out these flaws and lack of intuition

which suggests you have some good practical experience with physics problem solving which has precipitated a certain feeling that you need to learn more about some kind of math. I would advise that you try to exploit this. In the same breath I want to recognize (as someone who did their BS and MS in physics) that physicists are not always so careful or explicit in how they are doing their mathematics. So learning means eventually going beyond physics sources and into a much wider world of mathematical thought. The particular things that mathematicians care about may or may not be relevant to the problem you are trying to solve in physics, and a good part of developing that intuition is to figure out which particular caveats that a mathematician expounds upon (more often than not, some esoterica about the space(s) that they are working in or the class of isomorphisms under which their results are invariant) matter physically. As you develop and intuition about these things a bonus is that you will be able to skim through mathematics resources much faster.