seraschka

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

  def forward(self, X, A):
    potential_msgs = torch.mm(X, self.W2)
    propagated_msgs = torch.mm(A, potential_msgs)
    root_update = torch.mm(X, self.W1)
    output = propagated_msgs + root_update + self.bias
    return output

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

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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.

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