28 February 2019

1. Generative modeling
for anything but generation
Alexandr Honchar

2. Artificial intelligence:
Almost MsC in applied mathematics @ UNIVR,
AI Consultant @ self-employed,
Blogger, speaker, 5+ years in ML
Healthcare:
AI Solution Architect, partner @ Mawi Solutions
Quantitative finance:
Researcher @ UNIVR

3. main question:
Can I use generative models even for my problems doesn’t
really need to generate any pictures or other stuff?

4. Generative modeling 101

5. https://blog.openai.com/glow/

6. Everybody Dance Now

7. TL-GAN: transparent latent-space GAN

8. Space of cats and dogs
A very deep neural network
already created some nice
embedding space for us
from initial pixel space

9. Discriminative modeling:
A function f(x, w) telling us
where new x belongs to
from {cat, dog}

10. Generative modeling:
A modeled distribution P(x|
y), where x - our data point
and y belongs to {cat, dog}
So, we can or create new
cat from P(x|y=“cat”), or
check if some x_i belongs
to P(x_i|y=“dog”) using well
known maths

11. Natural manifold
hypothesis:
real-world high dimensional
data (such as images) lie on
low-dimensional manifolds
embedded in the high-
dimensional space
Short tails, a little fur
Long tails, a lot of fur

12. Natural manifold
hypothesis:
real-world high dimensional
data (such as images) lie on
low-dimensional manifolds
embedded in the high-
dimensional space

13. Electrocardiograms 101

14. https://www.youtube.com/watch?v=vg9TH-MHHjw

15. P(X): anomaly detection / one-class classification

16. too many sources and types of noise for supervised learning

17. https://skymind.ai/wiki/deep-autoencoder
https://www.researchgate.net/
figure/Generative-Adversarial-
Network-GAN_fig1_317061929

18. P(X): data understanding

19. Me and a dataset of ECGs I got from idk where with idk what properties
What if it’s even not ECG?
What if it’s some unknown signal with unknown features?
I want an interpretable, generative and unsupervised representation!
15 TB

20. Lead: [1, 2 or 3]
Health: [healthy or after infarction]
Hardware: [with bs wander or without]

21. β-Variational autoencoders

22. Epoch 1, z_i ~ (-3, 3)

23. Epoch 50, z_i ~ (-3, 3)

24. P(X)->P(Y): filterting / domain adaptation

25. X
Y
P(X) -> P(Y) ?

26. https://docs.neptune.ml/get-started/style-transfer/
X Y P(X) -> P(Y)

28. P(X)->P(Y): better embeddings

29. what if I only could work with all the leads…

30. +3-5% in F1 score depending on the target
A Generative Modeling Approach to Limited Channel ECG Classification

31. Facebook: @rachnogstyle
Medium: @alexrachnog
Mail: alex@mawi.band
Takeaways:
- generative modeling >= discriminative modeling
- works for anomaly detection
- works for determining factors of variation
- works for domain adaptation of not-aligned datasets
- can build better embeddings for further supervised
learning
Alexandr Honchar