Kyle Roth

This post was created as an assignment in Bang Liu’s IFT6289 course in winter 2022. The structure of the post follows the structure of the assignment: summarization followed by my own comments. To summarize, the authors create a unifying framework for describing message-passing neural networks, which they apply to the problem of predicting the structural properties of chemical compounds in the QM9 dataset. paper summarization permalink The authors first demonstrate that many of the recent works applying neural nets to this problem can fit into a message-passing neural network (MPNN) framework. Under the MPNN framework, at each time step \(t\) a message is computed for each vertex by summing the output of a learned function \(M_t\) over the vertex and all edges and vertices connected to it. Then the next state for each vertex is a learned function \(U_t\) of the previous state and the message. Finally, the “readout” function \(R\) is applied to all the vertices to compute the result.