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Posts by Pat Alt
you spend your whole life training to go to space, and when you get there, fucking Outlook is busted. god damn it www.404media.co/artemis-2-as...
Due to other commitments, I was less present this time than I wanted to be, but highly recommend @satml.org to anyone working in the field!
On Monday, I got to finally share our work on counterfactual training @satml.org. Very happy that this conference still features work on explainability and grateful for many positive interactions during the poster session.
Preprint: arxiv.org/pdf/2601.16205
Julia (beta): github.com/JuliaTrustwo...
Thank you very much - hope you're well!
I'm thankful also to my committee members, among them @mmitchell.bsky.social, for many interesting questions and discussions.
And, of course, immensely grateful to @julialang.org and its community for having me and having had such a strong impact on my research and the whole Ph.D. experience.
The Ph.D. is wrapped indeed as of last Wednesday!
It was a pleasure and privilege to be working under the supervision @informusiccs.bsky.social and Arie van Deursen these past few years.
Thesis: www.patalt.org/thesis/
Defence: www.patalt.org/content/talk...
FOSS: @taija.org #julialang
Lieben Dank, Ronny 🙏
Really love the show so far 👏
Screen shot of selected git commit history.
Graduation highlight: my former students and now colleagues gifted me a *PhD Wrapped* of my git commit history and it's a bloodbath. Enjoy* www.patalt.org/content/talk...
*viewer discretion advised
Karen Hao
Nicky Woolf
Thomas Germain
I'm co-hosting a new BBC podcast! It's called The Interface, and it's all about how tech is rewiring your week and your world.
www.bbc.com/mediacentre/...
My pass:
Haven’t read the full paper, but in my mind, this is just an inevitable consequence of extremely high degrees of freedom and MI just exists in the context of that
I don’t think multiplicity of explanations is necessarily problematic, in fact it may often be desirable e.g. in the context of algorithmic recourse. But it’s definitely important to be transparent about it when interpreting and communicating results in MI and XAI more broadly
"Reject" despite mostly positive reviews
Somehow I'm not as fazed this time, because we have done a ton of robustness checks, the theory checks out and criticism was largely about presentation. I guess the 45 page appendix didn't help ...
I‘m avoiding actual eye contact at all costs
I did use RCall.jl back then to extend Plots.jl functions with ggplot2 (incredible scenes) and even those monstrosities still work, so props to #rstats I guess.
I love the fact that I can go back to my 3-4yo #julialang project, run `julia +1.8`, then `[ instantiate` and
EVERYTHING. JUST. WORKS. I LOVE IT*
*Julia, not my 3-4yo code
I've had little time for #julialang dev work in recent weeks as I've been wrapping up my thesis. Can't wait to get back to it soon and DifferentiationInterface.jl will be one of the first places to look at.
This work and the chart should go a long way in terms of explaining "why Julia" to AI folks:
1. Autodiff through anything using anything (one day ...)
2. Multiple dispatch fosters extensibility and interoperability of different ecosystems that OOP just doesn't (in practice).
3. See 1.
Moving fast and breaking things is difficult to justify when things are humans
... but not area of expertise I'm afraid so just thinking out loud
hmm I guess you're thinking of something along the lines of probing activations (see e.g. arxiv.org/abs/2404.14082) but that just maps from learned representations to some output. Honestly the best I can think of for attribution is membership inference attacks: www.cs.cornell.edu/~shmat/shmat...
A comparison of automatic differentiation paradigms between Python and Julia: - In Python, one chooses the autodiff framework first (PyTorch / JAX), then the appropriate scientific library - In Julia, one writes the scientific library first, then one tries to make it compatible with several autodiff frameworks (Enzyme, Zygote, etc)
How to make #autodiff user-friendly? What lies beyond the safety of Python-world? Why does it matter for scientific machine learning?
All this, and more, in our latest preprint with @adrhill.bsky.social! Spoiler alert: it describes the most useful software I ever wrote.
arxiv.org/abs/2505.05542
Hello Friends!
I'm on the job market now!
I have a oodles of knowledge for all the software performance engineering tricks in Rust, Julia and other systems languages and would love to work with teams that are looking to skill up in those respects, from back ends to big data crunching!
Brother and my smiling after the finish
Me running in asphalt somewhere in Düsseldorf.
Zoomed in version of the previous pic showing a Julia stick placed on my number tag.
Ran my first marathon last Sunday with my brother and a friend. Thought the #julialang sticker might help but we ran hella slow 🐌
In all seriousness, I’ve learned a lot from the work of @mmitchell.bsky.social and others in her field and I’ve also learned a lot from Hard Fork. There’s disagreements but I feel that there’s also certain overlaps and you+Kevin have a fantastic platform to discuss them using >300 characters.
I happen to know a great podcast where this conversation could be continued 👀