Machine learning and other gibberish
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
#dev

https://substack.com/redirect/4af1baf3-a4d7-48d1-9592-5ea0971a056a?j=eyJ1IjoiNHowa3gifQ.QnwKDJ1CRSD1ToSPhPzIWMi45g-Rid7OgDj8cqSear0

Sounds good but please DO NOT USE upper cases. It is not only about PEP8 but more about consistency and cognitive load.

We solve this problem by writing down the dimensions in the docstrings and also include the math expressions there. But it is already obvious that writing down the dimensions in the var names makes things much easier.
#misc

Walking in the dark is different for women and men.

Chaney, Robert A., Alyssa Baer, and L. Ida Tovar. 2023. “Gender-Based Heat Map Images of Campus Walking Settings: A Reflection of Lived Experience.” Violence and Gender, December. https://doi.org/10.1089/vio.2023.0027.
#ml

I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions.

Then I saw this paper today:


Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
#ml

Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032

Not surprising at all when you have direct access to a long context. But hey, look at this title.
Back to Top