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Comment by RandyRanderson | Hacker News Reader

Certainly a much shorter way to say this: if you have enough lines you can approximate any curve within a margin. This is what large neural networks are doing.

Another way to look at it: most neural nets are just a bunch of polynomials stitched together. You can see this from the popularity of the relu activation function. when the relu is negative, that poly is always zero in that area. When positive it's some poly multiplied by a const - another poly.

For nets that use other activation fns, they try to be linear in the area of most active input. So again they approximate a const * a ploy.

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> if you have enough lines you can approximate any curve within a margin. This is what large neural networks are doing.

No, this is actually exactly the opposite of what the article is saying.

"If you have enough lines you can approximate any curve within a margin" is usually a bad thing because your approximations lose meaning as the number of lines increase.

The surprising thing about machine learning is that increasing the "number of lines", if you will, increases the meaning, too, and there's some wierd and subtle properties about mathematics in higher dimensions that makes this work.

You are missing the point. The surprising thing about deep learning is that it can generalize to unseen data so well. Polynomial regression cannot.

If there is a pattern in data, such as it fitting on a curve, and you approximate that curve, then that should generalize to unseen data. What's surprising about that? A single polynominal regression wouldn't be able to do it, because some curves cannot be expressed as a polynominal, but superposition of multiple polynominals is apparently good enough.

If that worked, people would use it, but it doesn't, so they don't.

> then that should generalize to unseen data.

It's not guaranteed at all. Overcomplicated models will "overfit" the training data and generalize very poorly.

> some curves cannot be expressed as a polynominal

You can approximate any (continuous and blah blah) curve arbitrarily well with Taylor expansions.

In fact, polynomials are one of the the most common examples to demonstrate overfitting. See figure 2 on wikipedia (https://en.wikipedia.org/wiki/Overfitting)

It doesn't necessarily seem that surprising to me.

If I can be really hand-wavy about it -- A lot of deep neural networks achieve their nonlinearity in a very constrained way. They stack linear models on top of each other, and the nonlinearity comes from using a a relatively simple nonlinear function such as logistic or tanh to scale the models' outputs before feeding them into the next one. (Without that step, you'd just have a linear combination of linear functions, which would itself be linear.)

That's a pretty constrained form of nonlinearity compared to polynomial regression, which tries to directly fit some high-order polynomial. I don't have anything like the math chops to prove it this, but I believe that means that the neural network is going to tend to favor a relatively smoother decision boundary, whereas polynomial regression is a naturally high variance sort of affair.

No, it's not constrained at all. In fact, even single hidden layer networks with nearly arbitrary activation functions are universal approximators (Hornik et al. 1989). Polynomials are also universal approximators (Weierstrass).

I'm not trying to say that neural networks are inherently constrained. I'm saying that, in typical usage, they tend to be used a certain way that I believe introduces some useful constraints. You can use a single hidden layer and an arbitrary activation functions, but, in practice, it's a heck of a lot more common to use multiple hidden layers and tanh.

It's worth noting that neural networks didn't take off with Hornik et al. style simple-topology-complex-activation-function universal approximators. They took off a decade or so later, with LeCun-style complex-topology-simple-activation-function networks.

That arguably suggests that the paper is of more theoretical than practical interest. It's also worth noting that one of the practical challenges with a single hidden layer and a complex activation function is that it's susceptible to variance. Just like polynomial regression.

This kind of stuff is called inductive bias and is a sexy topic nowadays.

I agree this is one of the reasons for the success of neural networks. But it was not obvious at first, and it still is quite hard to formalize and explain in mathematical terms. That's what I meant with "surprising".

How's that different from regular interpolation?

That's a strong unsubstantiated claim. On the other hand, there's been some nice theoretical work arguing that deep learning is a form of polynomial regression.

https://arxiv.org/abs/1806.06850

Let us know when polynomial regression succeeds at any machine learning task.

A lot of people publish results that say deep learning is "just" something else, where the something else doesn't work.

How about 80% accuracy on CIFAR-10 with unsupervised training?

=> logistic regression + Kmeans

http://ai.stanford.edu/~acoates/papers/coatesleeng_aistats_2...

They are both universal approximators. So are support vector machines, gaussian processes and gradient boosted trees. Yet the performance of neural networks is unrivaled in certain tasks, as has been proven over and over again.

As a whole, that paper is quite bad (and still unpublished, probably blocked by peer review) because (1) it only considers fully connected networks (which are a minority of models used nowadays) and (2) the experimental validation was done on tasks where neural networks are not very strong.

Show me examples of competitive polynomial regression models in language translation, image segmentation and Go playing and I will be convinced.

To be fair, most of the machine learning literature had no or a poor excuse for peer review. And many deep learning layers can be described by dense layers. For example, convolution.

You almost certainly can frame Go playing as polynomial regression but there would undoubtedly be numerical precision & other gradient issues. Deep learning is a practice is remarkably effective, no disagreement there.

Perhaps you mean: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk."?

https://en.wikiquote.org/wiki/John_von_Neumann

Side note: You can fit an elephant with just 1 parameter. See https://twitter.com/exobenelson/status/1001473539789213697 and the paper linked therein.

> if you have enough lines you can approximate any curve within a margin

How avoid overfitting the training data?

You're right - a LOT of work goes into this (eg GANs) because these large nNets just start "remembering" the training data.

This article is about optimization (finding good parameters), not the approximation power of neural networks (which is well-known through the universal approximation theorem).

I know. What we're both saying is: if you have enough lines, you can find the params easily.

ReLU networks are piecewise linear.

Have a look at some published nn architectures - you'll typically see a few nonlinear units/transforms and a LOT of ReLU ones.

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