I think the second one by Carroll is quite careful to say things like “we can now understand why singular models have the capacity to generalise well” which seems to me uncontroversial, given the definitions of the terms involved and the surrounding discussion.
The title of the post is Why Neural Networks obey Occam’s Razor! It also cites Zhang et al, 2017, and immediately after this says that SLT can help explain why neural networks have the capacity to generalise well. This gives the impression that the post is intended to give a solution to problem (ii) in your other comment, rather than a solution to problem (i).
I think this also suggests an equivocation between the RLCT measure and practical generalisation behaviour. Moreover, neither post contains any discussion of the difference between (i) and (ii).
Well neural networks do obey Occam’s razor, at least according to the formalisation of that statement that is contained in the post (namely, neural networks when formulated in the context of Bayesian learning obey the free energy formula, a generalisation of the BIC which is often thought of as a formalisation of Occam’s razor).
I think that expression of Jesse’s is also correct, in context.
However, I accept your broader point, which I take to be: readers of these posts may naturally draw the conclusion that SLT currently says something profound about (ii) from my other post, and the use of terms like “generalisation” in broad terms in the more expository parts (as opposed to the technical parts) arguably doesn’t make enough effort to prevent them from drawing these inferences.
I have noticed people at the Berkeley meeting and elsewhere believing (ii) was somehow resolved by SLT, or just in a vague sense thinking SLT says something more than it does. While there are hard tradeoffs to make in writing expository work, I think your criticism of this aspect of the messaging around SLT on LW is fair and to the extent it misleads people it is doing a disservice to the ongoing scientific work on this important subject.
I’m often critical of the folklore-driven nature of the ML literature and what I view as its low scientific standards, and especially in the context of technical AI safety I think we need to aim higher, in both our technical and more public-facing work. So I’m grateful for the chance to have this conversation (and to anybody reading this who sees other areas where they think we’re falling short, read this as an invitation to let me know, either privately or in posts like this).
I’ll discuss the generalisation topic further with the authors of those posts. I don’t want to pre-empt their point of view, but it seems likely we may go back and add some context on (i) vs (ii) in those posts or in comments, or we may just refer people to this post for additional context. Does that sound reasonable?
At least right now, the value proposition I see of SLT lies not in explaining the “generalisation puzzle” but in understanding phase transitions and emergent structure; that might end up circling back to say something about generalisation, eventually.
Well neural networks do obey Occam’s razor, at least according to the formalisation of that statement that is contained in the post (namely, neural networks when formulated in the context of Bayesian learning obey the free energy formula, a generalisation of the BIC which is often thought of as a formalisation of Occam’s razor).
Would that not imply that my polynomial example also obeys Occam’s razor?
However, I accept your broader point, which I take to be: readers of these posts may naturally draw the conclusion that SLT currently says something profound about (ii) from my other post, and the use of terms like “generalisation” in broad terms in the more expository parts (as opposed to the technical parts) arguably doesn’t make enough effort to prevent them from drawing these inferences.
Yes, I think this probably is the case. I also think the vast majority of readers won’t go deep enough into the mathematical details to get a fine-grained understanding of what the maths is actually saying.
I’m often critical of the folklore-driven nature of the ML literature and what I view as its low scientific standards, and especially in the context of technical AI safety I think we need to aim higher, in both our technical and more public-facing work.
Yes, I very much agree with this too.
Does that sound reasonable?
Yes, absolutely!
At least right now, the value proposition I see of SLT lies not in explaining the “generalisation puzzle” but in understanding phase transitions and emergent structure; that might end up circling back to say something about generalisation, eventually.
I also think that SLT probably will be useful for understanding phase shifts and training dynamics (as I also noted in my post above), so we have no disagreements there either.
I would argue that the title is sufficiently ambiguous as to what is being claimed, and actually the point of contention in (ii) was discussed in the comments there too. I could have changed it to Why Neural Networks can obey Occam’s Razor, but I think this obscures the main point. Regular linear regression could also obey Occam’s razor (i.e. “simpler” models are possible) if you set high-order coefficients to 0, but the posterior of such models does not concentrate on those points in parameter space.
At the time of writing, basically nobody knew anything about SLT, so I think it was warranted to err on the side of grabbing attention in the introductory paragraphs and then explaining in detail further on with “we can now understand why singular models have the capacity to generalise well”, instead of caveating the whole topic out of existence before the reader knows what is going on.
As we discussed at Berkeley, I do like the polynomial example you give and this whole discussion has made me think more carefully about various aspects of the story, so thanks for that. My inclination is that the polynomial example is actually quite pathological and that there is a reasonable correlation between the RLCT and Kolmogorov complexity in practice (e.g. the one-node subnetwork preferred by the posterior compared to the two-node network in DSLT4), but I don’t know enough about Kolmogorov complexity to say much more than that.
I could have changed it to Why Neural Networks can obey Occam’s Razor, but I think this obscures the main point.
I think even this would be somewhat inaccurate (in my opinion). If a given parametric Bayesian learning machine does obey (some version of) Occam’s razor, then this must be because of some facts related to its prior, and because of some facts related to its parameter-function map. SLT does not say very much about either of these two things. What the post is about is primarily the relationship between the RLCT and posterior probability, and how this relationship can be used to reason about training dynamics. To connect this to Occam’s razor (or inductive bias more broadly), further assumptions and claims would be required.
At the time of writing, basically nobody knew anything about SLT
Yes, thank you so much for taking the time to write those posts! They were very helpful for me to learn the basics of SLT.
As we discussed at Berkeley, I do like the polynomial example you give and this whole discussion has made me think more carefully about various aspects of the story, so thanks for that.
I’m very glad to hear that! :)
My inclination is that the polynomial example is actually quite pathological and that there is a reasonable correlation between the RLCT and Kolmogorov complexity in practice
Yes, I also believe that! The polynomial example is definitely pathological, and I do think that low λ almost certainly is correlated with simplicity in the case of neural networks. My point is more that the mathematics of SLT does not explain generalisation, and that additional assumptions definitely will be needed to derive specific claims about the inductive bias of neural networks.
The title of the post is Why Neural Networks obey Occam’s Razor! It also cites Zhang et al, 2017, and immediately after this says that SLT can help explain why neural networks have the capacity to generalise well. This gives the impression that the post is intended to give a solution to problem (ii) in your other comment, rather than a solution to problem (i).
Jesse’s post includes the following expression:
Complex Singularities⟺Fewer Parameters⟺Simpler Functions⟺Better Generalization
I think this also suggests an equivocation between the RLCT measure and practical generalisation behaviour. Moreover, neither post contains any discussion of the difference between (i) and (ii).
Well neural networks do obey Occam’s razor, at least according to the formalisation of that statement that is contained in the post (namely, neural networks when formulated in the context of Bayesian learning obey the free energy formula, a generalisation of the BIC which is often thought of as a formalisation of Occam’s razor).
I think that expression of Jesse’s is also correct, in context.
However, I accept your broader point, which I take to be: readers of these posts may naturally draw the conclusion that SLT currently says something profound about (ii) from my other post, and the use of terms like “generalisation” in broad terms in the more expository parts (as opposed to the technical parts) arguably doesn’t make enough effort to prevent them from drawing these inferences.
I have noticed people at the Berkeley meeting and elsewhere believing (ii) was somehow resolved by SLT, or just in a vague sense thinking SLT says something more than it does. While there are hard tradeoffs to make in writing expository work, I think your criticism of this aspect of the messaging around SLT on LW is fair and to the extent it misleads people it is doing a disservice to the ongoing scientific work on this important subject.
I’m often critical of the folklore-driven nature of the ML literature and what I view as its low scientific standards, and especially in the context of technical AI safety I think we need to aim higher, in both our technical and more public-facing work. So I’m grateful for the chance to have this conversation (and to anybody reading this who sees other areas where they think we’re falling short, read this as an invitation to let me know, either privately or in posts like this).
I’ll discuss the generalisation topic further with the authors of those posts. I don’t want to pre-empt their point of view, but it seems likely we may go back and add some context on (i) vs (ii) in those posts or in comments, or we may just refer people to this post for additional context. Does that sound reasonable?
At least right now, the value proposition I see of SLT lies not in explaining the “generalisation puzzle” but in understanding phase transitions and emergent structure; that might end up circling back to say something about generalisation, eventually.
Would that not imply that my polynomial example also obeys Occam’s razor?
Yes, I think this probably is the case. I also think the vast majority of readers won’t go deep enough into the mathematical details to get a fine-grained understanding of what the maths is actually saying.
Yes, I very much agree with this too.
Yes, absolutely!
I also think that SLT probably will be useful for understanding phase shifts and training dynamics (as I also noted in my post above), so we have no disagreements there either.
I would argue that the title is sufficiently ambiguous as to what is being claimed, and actually the point of contention in (ii) was discussed in the comments there too. I could have changed it to Why Neural Networks can obey Occam’s Razor, but I think this obscures the main point. Regular linear regression could also obey Occam’s razor (i.e. “simpler” models are possible) if you set high-order coefficients to 0, but the posterior of such models does not concentrate on those points in parameter space.
At the time of writing, basically nobody knew anything about SLT, so I think it was warranted to err on the side of grabbing attention in the introductory paragraphs and then explaining in detail further on with “we can now understand why singular models have the capacity to generalise well”, instead of caveating the whole topic out of existence before the reader knows what is going on.
As we discussed at Berkeley, I do like the polynomial example you give and this whole discussion has made me think more carefully about various aspects of the story, so thanks for that. My inclination is that the polynomial example is actually quite pathological and that there is a reasonable correlation between the RLCT and Kolmogorov complexity in practice (e.g. the one-node subnetwork preferred by the posterior compared to the two-node network in DSLT4), but I don’t know enough about Kolmogorov complexity to say much more than that.
I think even this would be somewhat inaccurate (in my opinion). If a given parametric Bayesian learning machine does obey (some version of) Occam’s razor, then this must be because of some facts related to its prior, and because of some facts related to its parameter-function map. SLT does not say very much about either of these two things. What the post is about is primarily the relationship between the RLCT and posterior probability, and how this relationship can be used to reason about training dynamics. To connect this to Occam’s razor (or inductive bias more broadly), further assumptions and claims would be required.
Yes, thank you so much for taking the time to write those posts! They were very helpful for me to learn the basics of SLT.
I’m very glad to hear that! :)
Yes, I also believe that! The polynomial example is definitely pathological, and I do think that low λ almost certainly is correlated with simplicity in the case of neural networks. My point is more that the mathematics of SLT does not explain generalisation, and that additional assumptions definitely will be needed to derive specific claims about the inductive bias of neural networks.