Conventions for dimensions of input and weight matrices in neural networks?
I would not say there is such a convention for it per se (if anyone has anything to comment on this, I would also like to know).
I think to make it clearer how the layer's input x interacts with the weights W, it might better to define the dimensions as the following:

x: (m x n)
W: (n x k)
bias term b: (k)

m remains as the number of examples. n represents number of input features and k represents number of neutrons in the layer.
As we know, we compute the output of the layer y as Wx + b. Therefore, the resulting output matrix will be  (m x k)