seraschka
seraschka t1_iusvmp1 wrote
Reply to [N] Meta AI | Evolutionary-scale prediction of atomic level protein structure with a language model by xutw21
This is super awesome stuff! But I would put a little asterisk on this for now. To get an idea of its real, unbiased accuracy, I wonder if they participated in CASP15 which is essentially the gold standard for assessing structure predictions. I think results will be released in December ... I guess we will know more about this next month.
seraschka t1_is1xb22 wrote
Reply to comment by aniketmaurya in [News] Lightning AI Open Sources Stable Diffusion App "Muse" by LightningAI_Main
Not bad! I must say it's really snappy
seraschka t1_is1wwp4 wrote
Reply to comment by aniketmaurya in [News] Lightning AI Open Sources Stable Diffusion App "Muse" by LightningAI_Main
Nice! Btw what hardware does it run on?
seraschka t1_iuxf2c3 wrote
Reply to [D] Graph neural networks by No_Captain_856
> I need to give in input to the neural network also info about connections among data (an adjacency matrix) in addition to the data themselves.
Yup :). In a nutshell, you can think of the forward pass as
where A is the adjacency matrix.
PS: I have a code notebook on coding a simple graph neural net from scratch if useful: https://github.com/rasbt/machine-learning-book/blob/main/ch18/ch18_part1.ipynb