https://mmuckley.github.io/
ai.meta.com/blog/v-jepa-...
ai.meta.com/blog/v-jepa-...
Qinco2 achieves as much as 40-60% reduction error for vector compression, as well as better performance for approximate similarity search.
Qinco2 achieves as much as 40-60% reduction error for vector compression, as well as better performance for approximate similarity search.
Prototyped code is often a bit hacky, but gets the job done. But if you ever need to extend it, it can be quite a pain.
Engineered code usually has some overarching design philosophy
Prototyped code is often a bit hacky, but gets the job done. But if you ever need to extend it, it can be quite a pain.
Engineered code usually has some overarching design philosophy
The changes are for working with newer package versions. Things now work on numpy 2.0, and a few deprecations are fixed. Other than that, it's the same as before :). Get it with
`pip install torchkbnufft`
The changes are for working with newer package versions. Things now work on numpy 2.0, and a few deprecations are fixed. Other than that, it's the same as before :). Get it with
`pip install torchkbnufft`
The advice part, centering things on technical points, is also useful for academic publishing and the review process. It really helps defuse what tends to be an adversarial relationship with reviewers (or authors).
My takeaways (i) There's no you nor I, there are only features, (ii) be techincal and actionable, (iii) don't write anything you could regret
The advice part, centering things on technical points, is also useful for academic publishing and the review process. It really helps defuse what tends to be an adversarial relationship with reviewers (or authors).
go.bsky.app/BoEtagz