memory that everyone claims so the algorithm is here depicted in these formulas it essentially notes that you can pull out the normalization of the softmax out until the end until after you've multiplied with the value matrix and therefore you can trade off the n squared memory requirement for doing it all in parallel with an iterative algorithm that uses less memory if you're interested check out paper michael bronstein has a cool blog post called deriving convolution from first principles so in this he goes through what a convolution is and how you can represent it as a circulant matrix but not only that he shows that if you want an operator that is naturally shift invariant and you view this through the lens of these circulant matrices and what happens if you shift them around if you want an operator like this then naturally it has to be the convolution operator it's pretty cool it draws on some fundamental math and fourier transforms enter the picture so if you're interested i definitely invite you to check it out and it is also a very good gateway into the entire literature of equivariant deep learning of course of which michael bronstein is an expert in the google ai blog has an entry on training machine learning models more efficiently with data set distillation i believe i've previously also made a video on this but now there is a blog post about it and i think more importantly the distilled data sets have been released if you don't know what this is this is essentially you want to train a classifier with as little data as possible however you get to make the data so you try to sort of make kind of adversarial examples or uber super prototypes of of data so that the classifier can learn from as little data as possible here you see a cNUMBER_TOKEN distilled into just NUMBER_TOKEN images so you have one single image per class so you see at the top you simply try to select the best images from each class and that will give you a final test accuracy of NUMBER_TOKEN percent again this is the entire data set but if your entire data set is this crafted data set at the bottom again only NUMBER_TOKEN images you'll get a test set accuracy of NUMBER_TOKEN which is pretty respectable for only having NUMBER_TOKEN images to train on so again there are papers to go along with it but there are also now the data sets available online hebo is a library for bayesian optimization released by huawei so this was the winning submission to the nurip's NUMBER_TOKEN black box optimization challenge so if you're into this field and you're looking for a very very performant library maybe this is it rudali has released their big model we've previously reported on rudali which is a russian version of dali and they have released their small model previously however now they are releasing their big model but they don't release the weights or anything like this of course as everyone else they release it via an api so you can call the api and you'll get a bunch of outputs so here you can see chic living room with green armchairs by the window this is by the way this is google translated the model is in russian here you can see a bunch of other images they do look awfully like cut out a lot of them look they have super sharp edges for some reason it's really interesting and the humans all of which have slightly weird
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