@[email protected]

Data vis-à-vis science. Award winning researcher. Rejected by all the top conferences. I've been called the Taylor Swift of data viz.

[@[email protected] @rpgove]

New guidelines for using t-SNE, and evaluation of prior guidelines. And ✨NEW✨: Auto t-SNE, a neural network system to automatically find good perplexity, learning rate, and exaggeration! 📈

Paper: https://t.co/PPMCDx2s9I
Blog: https://t.co/Y6Vd8ECLQc
(1/9)

[@[email protected] @rpgove]

I'm not very active on Mastodon, but then again I'm not very active on Twitter either https://t.co/WfbAQQJort

[@[email protected] @rpgove]

@VizSec Are you at #ieeevis this year? Get some #VizSec swag!

[@[email protected] @rpgove]

I'm curious how this new Rick & Morty/The Walking Dead crossover series is going to turn out

[VizSec @VizSec]

The 2022 Symposium on #DataVisualization for #cybersecurity is only 16 days away.

Looking forward to seeing you all on Wednesday, Oct. 19th, 9am - 3:15pm (UTC-5).

If you're attending @ieeevis in @cityofokc you'll find us in the Pinon room
#VizSec #ieeevis

[Graphicacy @graphicacy_]

Our team is looking for Fall and Winter #dataviz engineering interns!

Are you interested in building on and leveraging your engineering toolkit to serve mission-driven clients working to make the world a better place?

Then we may have a role for you!👇

https://t.co/K0ox4r4U0N

[Ben Larson, Ph.D. @blarson424]

#statistics #rstats #Datavisualization #MachineLearning #datamining #regression

[Kai @syntagmatic]

A step-by-step guide to improving a chart design by applying WCAG accessibility principles. Finally publishing some of my work at Google with @KentTheHuth! Hoping to release some of our Heatlane code examples soon too https://t.co/4iAM3uUnup

[VizSec @VizSec]

The #VizSec 2022 deadline is approaching:
**June, 22 for full, short, and position papers**

Did you know that all past programs & papers are listed on https://t.co/I3dfWUuolW &
https://t.co/C2UHaLiDqj ?
These are great resources for writing a position paper.
More about that👇🏼🧵

[Cliff Pickover @pickover]

Mathematics, computer programming, engineering, motion, creativity.

This is a linkage-mechanism for converting binary numbers to decimal numbers. Created by 上木 敬士郎/Keishiro Ueki, @KeishiroUeki, https://t.co/gx8Zn92XSC

[Jeff Zients Archived @WHCOS46Archive]

COVID cases are up, so the Biden Admin is making more free COVID tests available -- 8 per household, delivered to your door, free of charge. Just go to https://t.co/wxtDD3OQ9f. It takes less than 1 minute to order.

[VizSec @VizSec]

Today we're featuring #VizSec 2021's best paper:
🏆Automatic Narrative Summarization for Visualizing Cyber Security Logs and Incident Reports🏆 by @rpgove
👇🏼

[@[email protected] @rpgove]

Coming up in a couple hours, I'm presenting our Auto t-SNE work at Vis Meets AI @PacificVis! Interested in #visualization meets #machinelearning?
Watch the presentation! https://t.co/RG3iijEjv3
Or read a sneak peak blog post: https://t.co/Y6Vd8ECLQc

[@[email protected] @rpgove]

For the #tsne #machinelearning #dataviz users: our new guidelines for using t-SNE can help you get better visualizations!
https://t.co/Y6Vd8ECLQc

We'll present this work in 2 weeks at Visualization Meets AI @PacificVis!

[@[email protected] @rpgove]

New guidelines for using t-SNE, and evaluation of prior guidelines. And ✨NEW✨: Auto t-SNE, a neural network system to automatically find good perplexity, learning rate, and exaggeration! 📈

Paper: https://t.co/PPMCDx2s9I
Blog: https://t.co/Y6Vd8ECLQc
(1/9)

[@[email protected] @rpgove]

We will present this work on April 11 at Visualization Meets AI, part of @PacificVis. It will appear in a special issue of the Journal of Visual Informatics
https://t.co/ieYZauIus0
(9/9)

[@[email protected] @rpgove]

New guidelines for using t-SNE, and evaluation of prior guidelines. And ✨NEW✨: Auto t-SNE, a neural network system to automatically find good perplexity, learning rate, and exaggeration! 📈

Paper: https://t.co/PPMCDx2s9I
Blog: https://t.co/Y6Vd8ECLQc
(1/9)

[@[email protected] @rpgove]

In summary:
* Use OpenTSNE
* If not using OpenTSNE, use perplexity=16, learning rate=10, exaggeration=1
* Learning rate = n/12 is also good
* Keep perplexity in 2-16, learning rate in 10-640, and exaggeration in 1-8

Our paper has lots more detail! https://t.co/PPMCDx2s9I
(8/9)