Carlos Scheidegger

Associate Professor, UA CS, HDC Lab

Associate Professor, UA CS, HDC Lab

https://hdc.cs.arizona.edu

- https://cscheid.net/talks/2020-10-16 (I’m using Google Chrome)
- Three pieces on dimensionality reduction (DR):
- DimReader
- Deep Inverse
- UMAP Tour

Shouldn't our tools provide mechanisms for inspecting their own behavior?

- Rebecca Faust, David Glickenstein, C.S., InfoVis 2018
- We give DR plots a new visual affordance: where will a point go if one of its attributes changes infinitesimally?

t-SNE visualization

author: "User from reddit", CC BY 3.0

- For many programs which produce values of functions, you can get derivatives for about the same effort as just computing thefunction value (!)
- The procedure is entirely synctatic, so a compiler can generate code that produces derivatives
- This is how pytorch and tensorflow work, and partly why neural nets are now so popular
- a quick tutorial on autodiff.

- (This is unpublished work by my student Mingwei Li - unpublished because it’s too simple!)
- Same general setup: we have the result of a DR method and we want to “explain it”
- Here, we attempt to find a good “inverse”: a mapping from projection back into input
- This allows us to interactively with the projection

- Obviously no exact solution exists, so we minimize an error measure
- The mapping is given by a deep neural network
- The codebase is a ~300-line Python program and a simple Javascript front-end

- This technique is broadly applicable
- The space of UI interactions can be low dimensional (eg. a screen is only 2D)
- Low-dimensional domains are “easy” to learn
- Other stupid ML tricks: use this to hide latency from a slow server

- Finally, I want to demo what’s currently possible with modern browser technology
- We will use DR to understand the behavior of deep neural networks (the opposite of what we just did!)
- Toward Comparing DNNs with UMAP Tour, by Mingwei Li as well. He will be present this work next Monday.

- I’m cscheid@email.arizona.edu
- I’ll be spending the Fall and Spring working in the ANTARES project
- I’m looking forward to meeting all of you, even if COVID-19 won’t let it be in person!
- Questions?