Power Law Distributions in Deep Learning

In a previous post, we saw that the Fully Connected (FC) layers of the most common pre-trained Deep Learning display power law behavior.  Specifically, for each FC weight matrix mathbf{W}, we compute the eigenvalues lambda of the correlation matrix mathbf{X}



For every FC matrix, the eigenvalue frequenciesrho_{emp}(lambda), or Empirical Spectral Density (ESD),  can be fit to a power law


where the exponents alpha  all lie in


Remarkably, the FC matrices all lie within the Class of Fat Tailed Random Matrices!

Heavy Tailed Random Matrices

We define a random matrix by defining a matrix mathbf{W} of size Mtimes N, and drawing the matrix elements W_{i,j} from a random distribution. We can choose a

  • Gaussian Random Matrix:    p(W_{i,j})sim N(0,sigma), where N(0,sigma) is a Gaussian distribution

or a

  • Heavy Tailed Random Matrix:    p(W_{i,j})sim Pr_{mu}(x), where Pr_{mu}(x)sim x^{-(mu+1)} is a  power law distribution

In either case, Random Matrix Theory tells us what the asymptotic form of ESD should look like.  But first, let’s see what model works best.

AlexNet FC3

First, lets look at the ESD rho_{emp}(lambda) for AlexNet for layer FC3, and zoomed in:

Recall that AlexNet FC3 fits a power law with exponent $alphasim&bg=ffffff $ , so we also plot the ESD on a log-log scale

AlexNet Layer FC3 Log Log Histogram of ESD

Notice that the distribution is linear in the central region, and the long tail cuts off sharply.  This is typical of the ESDs for the fully connected (FC) layers of the all the pretrained models we have looked at so far.  We now ask…

What kind of Random Matrix would make a good model for this ESD ?

ESDs: Gaussian random matrices

We first generate a few Gaussian Random matrices (mean 0, variance 1), for different aspect ratios Q,  and plot the histogram of their eigenvalues.

N, M = 1000, 00
Q = N / M
W = np.random.normal(0,1,size=(M,N))
# X shape is M x M
X = (1/N)*np.dot(W.T,W)
evals = np.linalg.eigvals(X)
plot.hist(evals, bins=100,density=True)
Empirical Spectral Density (ESD) for Gaussian Random Matrices, with different Q values.

Notice that the shape of the ESD depends only on Q, and is tightly bounded; there is, in fact, effectively no tail at all to the distributions (except, perhaps, misleadingly for Q=1)

ESDs: Power and Log Log Histograms

We can generate a heavy, or fat-tailed, random matrix as easily using the numpy Pareto function


Heavy Tailed Random matrices have a very ESDs.   They have very long tails–so long, in fact, that it is better to plot them on a log log Histogram

Do any of these look like a plausible model for the ESDs of the weight matrices of a big DNN, like AlexNet ?

  • the smallest exponent, mu=1 (blue), has a very long tail, extending over 11 orders of magnitude. This means the largest eigenvalues would be lambda_{max}sim 10^{11}.  No real W would behave like this.
  • the largest exponent, mu=5 (red), has a very compact ESD, resembling more the Gaussian Ws above.
  • the fat tailed  mu=3 ESD (green), however, is just about right.  The ESD is linear in the central region, suggesting a power law.  It is a little too large for our eigenvalues , but the tail also cuts off sharply, which is expected for any finite W .  So we are close
AlexNet FC3

Lets overlay the ESD  of fat-tailed W with the actual empirical rho_{emp}(lambda) from AlexNet for layer FC3

We see a pretty good match to a Fat-tailed random matrix with mu=2.5.

Turns out, there is something very special about mu being in the range -4.

Universality Classes:

Random Matrix Theory predicts the shape of the ESD , in the asymptotic limit, for several kinds of Random Matrix, called University Classes.  The 3 different values of mu each represent a different Universality Class:

In particular, if we draw mathbf{W} from any heavy tailed / power law distribution, the empirical (i.e. finite size) eigenvalue density rho_{N}(lambda) is likewise a power law (PL), either globally, or at least locally.

What is more, the predicted ESDs have different, characteristic global and local shapes, for specific ranges of mu.    And the amazing thing is that

the ESDs of the fully connected (FC) layers of pretrained DNNs all resemble the ESDs of the muin[2,4] Fat-Tailed Universality Classes of Random Matrix Theory

But this is a little tricky to show, because we need to show that alpha we fit to the theoretical mu.  We now look at the

Relations between alpha and mu

RMT tells us that, for mu<4, the ESD takes the limiting for

rho(x)sim x^{-(mu/2+1)}, where


And this works pretty well in practice for the Heavy Tailed Universality Class, for mu<2.  But for any finite matrix, as soon as musim 2, the finite size effects kick in, and we can not naively apply the infinite limit result.

Statistics of the maximum eigenvalue(s)

RMT not only tells us about the shape of the ESD; it makes statements about the statistics of the edge and/or tails — the fluctuations in the maximum eigenvalue Deltalambda=Vertlambda-lambda_{max}Vert.  Specifically, we have

  • Gaussian RMT:  Deltalambdasim Tracy Widom
  • Fat Tailed RMT:  Deltalambdasim Frechet

For standard, Gaussian RMT, the lambda_{max} (near the bulk edge) is governed by the famous Tracy Widom.  And for mu>4, RMT is governed by the Tau Four Moment Theorem.

But for 2<mu<4, the tail fluctuations follow Frechet statistics, and the maximum eigenvalue has Power Law finite size effects

lambda_{max}sim M^{mu/4-1}(1/Q)^{1-2/mu}

In particular, the effects of M and Q kick in as soon as musim 2.  If we underestimate lambda_{max}, (small Q, large M), the power law will look weaker, and we will overestimate alpha in our fits.

And, for us, this affects how we estimate mu from alpha and assign the Universality Class

Fat Tailed Matrices and the Finite Size Effects for 2<mu<4

Here, we generate generate ESDs for 3 different Pareto Heavy tailed random matrices, with the fixed M (left) or N (right), but different Q.  We fit each ESD to a Power Law.  We then plot alpha, as fit, to mu.










The red lines are predicted by  Heavy Tailed RMT (MP) theory, which works well for Heavy Tailed ESDs with mu<2.  For Fat Tails, with 2<mu<4, the finite size effects are difficult to interpret.  The main take-away is…

We can identify finite size matrices W that behave like the the Fat Tailed Universality Class of RMT (muin[2,4]with Power Law fits, even with exponents alpha,  ranging upto 4 (and even upto 5-6).


It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.

Self Organized Criticality

In statistical physics,  if a system displays a Power Laws, this can be evidence that it is operating near a critical point.  It is known that real, spiking neurons display this behavior, called Self Organized Criticality

It appears that Deep Neural Networks may be operating under similar principles, and in future work, we will examine this relation in more detail.

Jupyter Notebooks

The code for this post is in this github repo on ImplicitSelfRegularization

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( https://calculatedcontent.com/2018/09/14/power-laws-in-deep-learning-2-universality/)


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