The concurrent rise of big data, modern hardware, and deep learning promises to transform analytics within healthcare and life science organizations. But big data is expensive to annotate, and not all data is created equal. The practicality of most problems requires inference from small datasets and incorporation of external knowledge. This is particularly true for tasks that involve natural language processing. John will discuss several methods that allow us to introduce prior knowledge to learn from small data, including deep contextual representations, inductive transfer learning, and adversarial augmentation. Presentation at the Joint Meeting: Nashville Data Science and Greater Nashville Healthcare Analytics on June 18, 2019.

1. Machine Learning with Small Data
John C. Liu, Ph.D. CFA
June 18, 2019
Twitter: @drjohncliu

2. Disclaimer
THE INFORMATION SET FORTH HEREIN HAS BEEN OBTAINED OR DERIVED FROM SOURCES GENERALLY
AVAILABLE TO THE PUBLIC AND BELIEVED BY THE AUTHOR TO BE RELIABLE, BUT THE AUTHOR DOES NOT MAKE
ANY REPRESENTATION OR WARRANTY, EXPRESS OR IMPLIED, AS TO ITS ACCURACY OR COMPLETENESS. THE
INFORMATION IS FOR EDUCATIONAL PURPOSES ONLY AND IS NOT INTENDED TO BE USED AS THE BASIS OF ANY
BUSINESS OR INVESTMENT DECISIONS BY ANY PERSON OR ENTITY. ALL OF THE INFORMATION CONTAINED IN
THE PRESENTATION IS SUBJECT TO FURTHER MODIFICATION AND ANY AND ALL FORECASTS, PROJECTIONS OR
FORWARD-LOOKING STATEMENTS CONTAINED HEREIN SHALL NOT BE RELIED UPON AS FACTS NOR RELIED
UPON AS ANY REPRESENTATION OF FUTURE RESULTS WHICH MAY MATERIALLY VARY FROM SUCH
PROJECTIONS AND FORECASTS.

3. Roadmap
• Introduction
• Big Data Revolution
• What about Small Data?
• Dealing with Reality
– Semantic/Contextualized Representations
– Experimental Design
– Adversarial Data Generation
• Conclusion

4. Big Data
Source: Bernard Marr & Co.

5. Deep Learning
Source: NVIDIA

6. Data is the New Oil
Source: James Corbett

7. More Data = Better Models
Source: Andrew Ng

8. What’s Wrong With this
Picture
Train Set?
Source: The Simpsons

9. Data Annotation is Expensive
Source: Jia, Yangqing. 2014). Learning Semantic Image Representations at a Large Scale.

10. Annotator (Dis)Agreement?
Source: Stephen Yip & Chintan Parmar

11. Annotation = Bottleneck
Source:physiconet.org

12. • 14 million images
• 20,000 categories
• 25 Human Years to annotate!
Source: Li Fei-Fei. (2010). ImageNet: Crowdsourcing, benchmarking & other cool things

13. Reality = Small Annotated
Data
Source: NASA/JPL/UCSD/JSC

14. Ways to Deal with Small Data
• AWS Mechanical Turk (e.g., ImageNet)
• CrowdFlower/Figure8/Appen
• Hire SMEs
• Data Augmentation/Synthetic Generation (SMOTE)

15. Synthetic Minority Oversampling
Nearest
Neighbor
Algorithm
Source: Bart Baesens

16. Anything
Else?
Photograph: Andrea Shea

17. Not All Data is Created Equal
https://pypi.org/project/imbalanced-learn/
Source: Rishabh Misra

18. Training a Cat/Dog Classifier
• Which training samples are more useful?
Photograph:American Kennel Club
Photograph:Atchoumfan
Photograph:Sujoy Roychowdhury

19. Oncology Text Classifier
Which training samples are more useful?
1. Left medial foot and ankle pain and swelling. Plantar
metatarsal pain for 5 weeks. No known trauma.
2. Dorsal right medial upper back pain for 10 weeks. Right
parotid mass.
3. History pancreatic cancer. Status post aortic
chemotherapy and Whipple procedure

20. Points Near Decision
Boundary
Maximum
Entropy

21. Machine Learning with Small Data
What Data Scientists Should Care Most About

22. Kid Saw This in a Toy Store
Tiger
Photograph:Nat & Jules Brown

23. At the Zoo a Few Weeks Later
Tiger
Photograph:Skip O’Rourke

24. Inductive Transfer Learning
• Learning new tasks using knowledge learned from other
tasks
Source: Dipanjan Sarkar

25. Semantic Image Representations
Source: Jia, Yangqing. 2014). Learning Semantic Image Representations at a Large Scale.

26. Word Embeddings
Corpus Docs Sentences
Words
Vectors
Word embeddings encode semantic
relationships learned from corpus.

27. Word2Vec Context too
Narrow
Source: Kamath, Uday, Liu, John & Whitaker, James. (2019). Deep Learning for NLP and Speech Recognition.

28. Neural Language Model
Source: Kamath, Uday, Liu, John & Whitaker, James. (2019). Deep Learning for NLP and Speech Recognition.

29. ELMO
Embeddings from Language Models
– Bidirectional Language Models (forward & backward)
– Using LSTMs
– Concatenate hidden layers
Source: Karan Purohit

30. Concept Embeddings
• RDF2Vec
Source: Kamath, Uday, Liu, John & Whitaker, James. (2019). Deep Learning for NLP and Speech Recognition.

31. Did We Solve the Tiger Problem?
• Generalize with only a single label? (One-Shot Learning)
• If I described a lion, would you recognize one if you never
ever saw one? (Zero-Shot Learning)
• Did the chicken come before the egg, or vice versa?
(Causality)

32. THE WORLD
IS NOT
RANDOM
INHERENT STRUCTURE EXISTS
Source: NASA/JPL/UCSD/JSC

33. Not Random
• Each CIFAR-10 image = 32x32 pixels by 3x256 colors
• Number of possible permutations = 786432!
Source: Krizhevsky, Alex. (2009). Learning Multiple Layers of Features from Tiny Images.

34. Not a Possible Permutation
Source: Goodfellow, Ian. (2016). Generative Adversarial Nettworks.

35. How many Laws of Physics are
sufficient to describe motion?
Photograph: Richard Jognston

36. Bayesian Networks
Factorizing
Joint PDF
Source: Sato, Renato and Sato, Graziela. (2015). Probabilistic graphic models applied to identification of diseases.

37. Adversarial Data Generation
Source: Mino, Ajkel & Spanakis, Gerasimos. (2018). LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color.

38. Last Word
Photograph: Gregor Schmidt

39. My New Book
A comprehensive resource that
builds up from elementary deep
learning, text, and speech
principles to advanced state-of-
the-art neural architectures.
On Amazon, BN, Springer
https://www.amazon.com/Deep-Learning-
NLP-Speech-Recognition/dp/3030145956

40. Thank you.
AI/ML Solutions to Solve Business Problems