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Thanks to all who joined us for our @ieeevis Seattle satellite! Come meet some of the participants and learn about their work: piped.video/watch?v=EgBQo5QX…
With DracoGPT, Will Wang shows how to extract and model visualization design preferences from generative AI systems — enabling new ways to quantify, evaluate, and efficiently reuse LLM-based chart recommendations. #ieeevis idl.uw.edu/papers/dracogpt
Congratulations to IDL alum @domoritz for winning a VGTC Significant New Researcher award!! A premier honor for early career researchers in visualization!
Interactive Data Lab retweeted
Mosaic is the future of data visualization. 10M cross-filtered rows with client-side processing. Powered by @duckdb
Interactive Data Lab retweeted
Chaining LLM calls can improve output quality, but navigating the massive space of task decompositions is challenging. Revisiting the established field of crowdsourcing, we distill strategies for effective LLM chain design and identify opportunities for future research. [1/11]
Interactive Data Lab retweeted
“Can we get a new text analysis tool?”
“No—we have Topic Model at home”
Topic Model at home: outputs vague keywords; needs constant parameter fiddling🫠
Is there a better way? We introduce LLooM, a concept induction tool to explore text data in terms of interpretable concepts🧵
Interactive Data Lab retweeted
The Vega Project is happy to announce the release of version 5.3.0 of the Vega-Altair Python visualization library. This release has been 4 months in the making and includes enhancements, fixes, and documentation improvements from 11 contributors. Highlights in 🧵
Interactive Data Lab retweeted
Observable Framework 1.3 🆕 integrates @uwdata’s Mosaic vgplot, which can concisely expressive performant coordinated views of millions of data points. observablehq.com/framework/l…
Interactive Data Lab retweeted
Interact with millions of data points in real-time with Mosaic, now with support for geospatial data.
Exploring 1M taxi pickups and dropoffs in NYC:
Interactive Data Lab retweeted
Chaining LLMs together to overcome LLM errors is an emerging, yet challenging, technique. What can we learn from crowdsourcing, which has long dealt with the challenge of decomposing complex work? We delve into this question in our new preprint: arxiv.org/abs/2312.11681 [1/9]
Interactive Data Lab retweeted
And congrats also to the other Significant New Researcher recipient, UW alum Matt Kay (@mjskay)!
Congrats to IDL co-director Leilani Battle (@leibatt) for the 2023 VGTC Significant New Researcher Award! #IEEEVIS2023 ieeecs-media.computer.org/tc…
Interactive Data Lab retweeted
Really enjoying playing with @uwdata 's Mosaic, linking @duckdb with @observablehq plot feels so natural and responsive - just a joy
Interactive Data Lab retweeted
VisText has been a *ton* of work, and 2 yrs of solid effort. So it's exciting to (finally!) be able to talk about it, and it's gratifying to see it featured on @MIT's homepage.
Lead author, @bennyjtang, has a great thread w/details below 👇
And I wanted to offer a few thoughts
Chart captioning is hard, both for humans & AI.
Today, we’re introducing VisText: a benchmark dataset of 12k+ visually-diverse charts w/ rich captions for automatic captioning (w/ @angie_boggust @arvindsatya1)
📄: vis.csail.mit.edu/pubs/viste…
💻: github.com/mitvis/vistext
#ACL2023NLP
Interactive Data Lab retweeted
Mosaic looks really great! Great to see a viz library thread down to the db level. DuckDB is such a natural choice for something like this.
uwdata.github.io/mosaic/
While Mosaic is still "research grade" software, we think the approach is quite promising. Feel free to share feedback or contribute on GitHub! github.com/uwdata/mosaic
