How about a PostDoc in beautiful Copenhagen on brain network analysis of diffusion data in developmental neuropsychiatry?
You would be working at the interdisciplinary @DRCMR as part of the Danish High Risk and Resilience Study (VIA) (www.drcmr.dk/via). The VIA study longitudinally follows the largest register-based cohort of children (n=522) in the world born to parents with schizophrenia or bipolar disorder or none of these disorders.
this paper analyzes sequences of image #GenAI prompts to figure out how people are using them in their creative process. They then make adjustments to the UI to improve the efficiency of the creative process
i love this kind of paper because it focuses on the role of humans in computing. There’s no point in computers when the humans aren’t considered
Playing with self-attention in latent diffusion models. This animation illustrates that the model learns to represent 3d scene properties like depth and object semantics. The red dot shows which pixel's self-attention map we're seeing.
It has not been trained on depth maps or segmentation maps, just normal natural images.
This excellent comic on the history of Luddism by Tom Humberstone https://thenib.com/im-a-luddite led me to this site with folks developing 'Glaze' which is a thing artists are using to mess with AI trying to scrape their art. Check it out here:
I started to learn about stochastic calculus because many of the most interesting breakthroughs these days (e.g., diffusion probabilistic models for generative AI) involve stochastic processes. There are a lot of new and confusing concepts: Lebesgue integrals, measure theory, sigma algebras and Borel sets, martingales, rigorous probability theory, etc. Here is a post on a few learnings related to martingales: