Generative modeling can be used for problems beyond just generation, such as anomaly detection, determining factors of variation in datasets, domain adaptation between not-aligned datasets, and building better embeddings for supervised learning tasks. Generative models can model the underlying distribution of data to check if a point belongs to that distribution or create new points from the distribution. They can learn low-dimensional manifolds on which real-world high-dimensional data like images lie. This allows generative models to be applied to challenges like filtering, style transfer, and improving embeddings to boost performance on downstream tasks.

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