Talk (to be given) June 8, 2018 at UC Berkeley / NERSC In Collaboration with Michael Mahoney, UC Berkeley Empirical results, using the machinery of Random Matrix Theory (RMT), are presented that are aimed at clarifying and resolving some of the puzzling and seemingly-contradictory aspects of deep neural networks (DNNs). We apply RMT to several well known pre-trained models: LeNet5, AlexNet, and Inception V3, as well as 2 small, toy models. We show that the DNN training process itself implicitly implements a form of self-regularization associated with the entropy collapse / information bottleneck. We find that the self-regularization in small models like LeNet5, resembles the familar Tikhonov regularization, whereas large, modern deep networks display a new kind of heavy tailed self-regularization. We characterize self-regularization using RMT by identifying a taxonomy of the 5+1 phases of training. Then, with our toy models, we show that even in the absence of any explicit regularization mechanism, the DNN training process itself leads to more and more capacity-controlled models. Importantly, this phenomenon is strongly affected by the many knobs that are used to optimize DNN training. In particular, we can induce heavy tailed self-regularization by adjusting the batch size in training, thereby exploiting the generalization gap phenomena unique to DNNs. We argue that this heavy tailed self-regularization has practical implications both designing better DNNs and deep theoretical implications for understanding the complex DNN Energy landscape / optimization problem.

1. calculation | consulting
why deep learning works:
self-regularization in deep neural networks
(TM)
c|c
(TM)
charles@calculationconsulting.com

2. calculation|consulting
UC Berkeley / NERSC 2018
why deep learning works:
self-regularization in deep neural networks
(TM)
charles@calculationconsulting.com

3. calculation | consulting why deep learning works
Who Are We?
c|c
(TM)
Dr. Charles H. Martin, PhD
University of Chicago, Chemical Physics
NSF Fellow in Theoretical Chemistry
Over 15 years experience in applied Machine Learning and AI
ML algos for: Aardvark, acquired by Google (2010)
Demand Media (eHow); first $1B IPO since Google
Wall Street: BlackRock
Fortune 500: Roche, France Telecom
BigTech: eBay, Aardvark (Google), GoDaddy
Private Equity: Anthropocene Institute
www.calculationconsulting.com
charles@calculationconsulting.com
(TM)
3

4. c|c
(TM)
Motivations: towards a Theory of Deep Learning
(TM)
4
calculation | consulting why deep learning works
Theoretical: deeper insight into Why Deep LearningWorks ?
non-convex optimization ?
regularization ?
why is deep better ?
VC vs Stat Mech vs ?
…
Practical: useful insight to improve engineering DNNs
when is a network fully optimized ?
large batch sizes ?
better ensembles ?
…

5. c|c
(TM)
Set up: the Energy Landscape
(TM)
5
calculation | consulting why deep learning works
minimize Loss: but how avoid overtraining ?

6. c|c
(TM)
Problem: How can this possibly work ?
(TM)
6
calculation | consulting why deep learning works
highly non-convex ? apparently not
expected observed ?
has been suspected for a long time that local minima are not the issue

7. c|c
(TM)
Problem: Local Minima ?
(TM)
7
calculation | consulting why deep learning works
Duda, Hart and Stork, 2000
solution: add more capacity and regularize

8. c|c
(TM)
Motivations: what is Regularization ?
(TM)
8
calculation | consulting why deep learning works
every adjustable knob and switch is called regularization
https://arxiv.org/pdf/1710.10686.pdf
Dropout Batch Size Noisify Data
…

9. c|c
(TM)
(TM)
9
calculation | consulting why deep learning works
Understanding deep learning requires rethinking generalization
Problem: What is Regularization in DNNs ?
ICLR 2017 Best paper
Large models overfit on randomly labeled data
Regularization can not prevent this

10. Moore-Pensrose pseudoinverse (1955)
regularize (Phillips, 1962)
familiar optimization problem
c|c
(TM)
Motivations: what is Regularization ?
(TM)
10
calculation | consulting why deep learning works
Soften the rank of X, focus on large eigenvalues ( )
Ridge Regression / Tikhonov-Phillips Regularization
https://calculatedcontent.com/2012/09/28/kernels-greens-functions-and-resolvent-operators/

11. c|c
(TM)
Motivations: how we study Regularization
(TM)
11
calculation | consulting why deep learning works
turn off regularization, turn it back on systematically, study W
and traditional regularization is applied to W
the Energy Landscape is determined by the layer weights WL
L
L

12. c|c
(TM)
(TM)
12
calculation | consulting why deep learning works
Information bottleneck
Entropy collapse
local minima
k=1 saddle points
floor / ground state
k = 2 saddle points
Information / Entropy
Energy Landscape: and Information flow
what happens to the layer weight matrices WL ?

13. c|c
(TM)
(TM)
13
calculation | consulting why deep learning works
Self-Regularization: Experiments
Retrained LeNet5 on MINST using Keras
Two (2) other small models: 3-Layer MLP and a Mini AlexNet
And examine pre-trained models (AlexNet, Inception, …)
Conv2D MaxPool Conv2D MaxPool FC1 FC2 FC

14. c|c
(TM)
(TM)
14
calculation | consulting why deep learning works
Matrix Complexity: Entropy and Stable Rank

15. c|c
(TM)
(TM)
15
calculation | consulting why deep learning works
Random Matrix Theory: detailed insight into WL
Empirical Spectral Density (ESD: eigenvalues of X=W W )LL
T
import keras
import numpy as np
import matplotlib.pyplot as plt
…
W = model.layers[i].get_weights()[0]
…
X = np.dot(W, W.T)
evals, evecs = np.linalg.eig(W, W.T)
plt.hist(X, bin=100, density=True)

16. c|c
(TM)
(TM)
16
calculation | consulting why deep learning works
Random Matrix Theory: detailed insight into WL
Entropy decrease corresponds to breakdown of random structure
and the onset of a new kind of self-regularization
Empirical Spectral Density (ESD: eigenvalues of X=W W )LL
T
Random
Matrix
Random
+ Spikes

17. c|c
(TM)
(TM)
17
calculation | consulting why deep learning works
Random Matrix Theory: Marchenko-Pastur
converges to a deterministic function
Empirical Spectral Density (ESD)
with well defined edges (depends on Q, aspect ratio)

18. c|c
(TM)
(TM)
18
calculation | consulting why deep learning works
Random Matrix Theory: Marcenko Pastur
plus Tracy-Widom fluctuations
very crisp edges
Q

19. c|c
(TM)
(TM)
19
calculation | consulting why deep learning works
Experiments: just apply to pre-trained Models
https://medium.com/@siddharthdas_32104/
cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5

20. c|c
(TM)
(TM)
20
calculation | consulting why deep learning works
Experiments: just apply to pre-trained Models
LeNet5 (1998)
AlexNet (2012)
InceptionV3 (2014)
ResNet (2015)
…
DenseNet201 (2018)
https://medium.com/@siddharthdas_32104/
cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5
Conv2D MaxPool Conv2D MaxPool FC FC

21. c|c
(TM)
(TM)
21
calculation | consulting why deep learning works
Marchenko-Pastur Bulk + Spikes
Conv2D MaxPool Conv2D MaxPool FC FC
softrank = 10%
RMT: LeNet5

22. c|c
(TM)
(TM)
22
calculation | consulting why deep learning works
RMT: AlexNet
Marchenko-Pastur Bulk-decay | Heavy Tailed
FC1
zoomed in
FC2
zoomed in

23. c|c
(TM)
(TM)
23
calculation | consulting why deep learning works
Random Matrix Theory: InceptionV3
Marchenko-Pastur bulk decay, onset of Heavy Tails
W226

24. c|c
(TM)
(TM)
24
calculation | consulting why deep learning works
Eigenvalue Analysis: Rank Collapse ?
Modern DNNs: soft rank collapses; do not lose hard rank
> 0
(hard) rank collapse (Q>1)
signifies over-regularization
= 0
all smallest eigenvalues > 0,
within numerical (recipes) threshold~

25. c|c
(TM)
(TM)
25
calculation | consulting why deep learning works
RMT: 5+1 Phases of Training

26. c|c
(TM)
(TM)
26
calculation | consulting why deep learning works
Bulk+Spikes: Small Models
Rank 1 perturbation Perturbative correction
Bulk
Spikes
Smaller, older models can be described pertubatively w/RMT

27. c|c
(TM)
(TM)
27
calculation | consulting why deep learning works
Spikes: carry more information
Information begins to concentrate in the spikes
S(v)
spikes have less entropy, are more localized than bulk

28. c|c
(TM)
(TM)
28
calculation | consulting why deep learning works
Bulk+Spikes: ~ Tikhonov regularization
Small models like LeNet5 exhibit traditional regularization
softer rank , eigenvalues > , spikes carry most information
simple scale threshold

29. c|c
(TM)
(TM)
29
calculation | consulting why deep learning works
Heavy Tailed: Self-Regularization
W strongly correlated / highly non-random
Can be modeled as if drawn from a heavy tailed distribution
Then RMT/MP ESD will also have heavy tails
Known results from RMT / polymer theory (Bouchaud, Potters, etc)
AlexNet
ReseNet50
InceptionV3
DenseNet201
…
Large, well trained, modern DNNs exhibit heavy tailed self-regularization

30. c|c
(TM)
(TM)
30
calculation | consulting why deep learning works
Heavy Tailed: Self-Regularization
Large, well trained, modern DNNs exhibit heavy tailed self-regularization
Salient ideas: what we ‘suspect’ today
No single scale threshold
No simple low rank approximation for WL
Contributions from correlations at all scales
Can not be treated pertubatively

31. c|c
(TM)
(TM)
31
calculation | consulting why deep learning works
Self-Regularization: Batch size experiments
We can cause small models to exhibit strong correlations / heavy tails
By exploiting the Generalization Gap Phenomena
Large batch sizes => decrease generalization accuracy
Tuning the batch size from very large to very small

32. c|c
(TM)
(TM)
32
calculation | consulting why deep learning works
Batch Size Tuning: Generalization Gap
Decreasing the batch size induces strong correlations in W
Random-Like Bleeding-outRandom-Like

33. c|c
(TM)
(TM)
33
calculation | consulting why deep learning works
Batch Size Tuning: Generalization Gap
Decreasing the batch size induces strong correlations in W

34. c|c
(TM)
(TM)
34
calculation | consulting why deep learning works
Batch Size Tuning: Generalization Gap
Decreasing the batch size induces strong correlations in W
Bulk+Spikes Bulk+Spikes Bulk-decay

35. c|c
(TM)
(TM)
35
calculation | consulting why deep learning works
Batch Size Tuning: Generalization Gap
Decreasing the batch size induces strong correlations in W
Bulk-decay Bulk-decay Heavy-tailed

36. c|c
(TM)
(TM)
36
calculation | consulting why deep learning works
Summary
self-regularization ~ entropy / information decrease
modern DNNs have heavy-tailed self-regularization
5+1 phases of learning
applied Random Matrix Theory (RMT)
small models ~ Tinkhonov regularization

37. c|c
(TM)
(TM)
37
calculation | consulting why deep learning works
Implications: RMT and Deep Learning
How can RMT be used to understand the Energy Landscape ?
tradeoff between
Energy and Entropy minimization
Where are the local minima ?
How is the Hessian behaved ?
Are simpler models misleading ?
Can we design better learning strategies ?

38. c|c
(TM)
Energy Funnels: Minimizing Frustration
(TM)
38
calculation | consulting why deep learning works
http://www.nature.com/nsmb/journal/v4/n11/pdf/nsb1197-871.pdf
Energy Landscape Theory for polymer / protein folding

39. c|c
(TM)
the Spin Glass of Minimal Frustration
(TM)
39
calculation | consulting why deep learning works
Conjectured 2015 on my blog (15 min fame on Hacker News)
https://calculatedcontent.com/2015/03/25/why-does-deep-learning-work/
Bulk+Spikes, flipped
low lying Energy state in Spin Glass ~ spikes in RMT

40. c|c
(TM)
RMT w/Heavy Tails: Energy Landscape ?
(TM)
40
calculation | consulting why deep learning works
Compare to LeCun’s Spin Glass model (2015)
Spin Glass with/Heavy Tails ?
Local minima do not concentrate
near the ground state
(Cizeau P and Bouchaud J-P 1993)
is Landscape is more funneled, no ‘problems’ with local minima ?

41. (TM)
c|c
(TM)
c | c
charles@calculationconsulting.com