This was a paper I presented about in Bang Liu’s research group meeting on 2022-04-11. You can view the slides I used here.

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. The authors create a novel system for combining an LM and a knowledge graph by performing reasoning over a joint graph produced by the LM and the KG, thus solving the problem of irrelevant entities appearing in the knowledge graph and unifying the representations across the LM and KG.

Turns out that money does buy happiness. You may have heard that people’s average happiness stops improving once you make more than $75,000/year? Researchers did a better survey with more data and found that that was not the case. The researchers cited 5 methodological improvements over the old research that suggested that it didn’t matter after $75,000: They measured people’s happiness in real time, instead of having people try to remember past happiness levels.

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 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.

This was a paper we presented about in Irina Rish’s neural scaling laws course (IFT6167) in winter 2022. You can view the slides we used here.