A bit over a year ago, we launched Awen.
We’ve been improving it every single week since.
It’s now much simpler, much stronger,
and used by thousands of people every day.
You can try it here: https://t.co/5cpDW9ZmQT
Would love to hear what you think.
@ycombinator@awen_ai A bit over a year ago, we launched Awen.
We’ve been improving it every single week since.
It’s now much simpler, much stronger,
and used by thousands of people every day.
You can try it here: https://t.co/5cpDW9ZmQT
Would love to hear what you think.
Very happy to launch and share what we have been building at @awen_ai and we are just getting started!
Whatever you can think? You can see - you just have to say it.
🎨@Awen_ai is rebuilding Photoshop, with an AI-voice interface.
Instead of navigating complex menus, creatives can simply describe their vision, and Awen will bring it to life.
https://t.co/cnjKRpyNi3
Really powerful video to build (or refresh) some intuitions and scaling notions around Large Language Models (LLMs) like GPT.
Slightly technical yet it is amenable for people with expertise in adjacent disciplines (e.g. physics, statistics, engineering, etc).
LLMs in Five Formulas: A somewhat idiosyncratic tutorial on LLMs.
The goal is to highlight five independent areas in LLMs that we kinda understand, while being humble about how hard the rest is.
https://t.co/JEWo3bgNOO
It is so easy to fall into a pure consumer/reader mode in this network if you are curiosity-driven, so much interesting stuff all the time...
Thus, after a four-year long hiatus, I am back into creating and sharing stuff here often! 🚀
Anyone in a similar situation? Any tips?
📢 Open PhD positions👩🎓👨🎓
Fully-funded Ph.D. positions in the areas of Machine Learning, Computer Vision, and Physics-Based Simulation for 3D human modeling, human interaction, and understanding of crowds. All details: https://t.co/h4ewEsP7oE
Likes and RTs highly appreciated!
An open-source server for hosting conda packages. A fast replacement to the conda command line utility.
Check out our new blog post on "Open Software Packaging for Science".
https://t.co/nCm4UKXUht
@pietrovischia@Giles_C_Strong After a few private communications back and forth with the authors they updated the preprint and removed the claims about different applicability in the related work section.
They still do not attribute ideas in intro or methodology though, hoping journal editors notice...
So #AcademicTwitter,
a preprint takes on same problem with same conceptual solution but with a minor change from one of my PhD publications.
Cite in related work incorrectly claiming different applicability and does not attribute ideas in intro or methodology.
Any advice?
Assume training a model takes weeks, but testing is fast. Estimating the variance of the performance of this model is not realistically feasible using cross-validation. How would you proceed to evaluate the uncertainty around the performance estimate?
@glouppe For a fixed model I guess a bootstrap estimate will approximate the variance of test set sampled from the same distribution.
I see no showstoppers but maybe I am missing something (as you said it would likely be more used otherwise given how cheap is bootstrapping).
@glouppe Thinking out loud here prompted by your tweet,
but wouldn't that miss part of the total variance due to the different model trainings that cross-validation could capture?
I guess is fine for a generalization error variance estimate of a given model but not of a methodology.
@pietrovischia@Giles_C_Strong I am not accostumed to add humorous nerdy GIFs into semi-serious threats.
Yet I could not help myself after @pietrovischia last point on merging indivuals into "the collaboration":
Been reading lots of stats and ML methods papers in high-energy physics for a review lately.
Maybe is the clarity of almost one year doing something else, but it seems the barrier for much better data analyses is mainly a (software) tooling and integration problem.
Any takes?
I have updated my course notes on automatic differentiation (last section of the PDF). Now includes dual numbers, adjoint state method, argmin layers, envelope theorem, reversible architectures. Thx to @PierreAblin for the constructive criticisms :) https://t.co/Bfc7FXpZpT
@phi_nate@Giles_C_Strong Yeah, that sucks...
I have come to believe that it is a combination of code shame and intelectual protectionism bias when done in the context scientific research, what is your take?
@HEPfeickert@davidism@mitsuhiko Indeed, it is really great, very grateful to the developers as well :)
There is also the new library https://t.co/0Shdd2aolG that builts upon click as the backend with a more minimalist take.