Virtual Trip Report ICML 2020

Last week, I attended my first machine learning conference: ICML 2020! The virtual format required by the corona measures made joining cheap and easy, though certainly at a cost: Poster sessions, especially, seemed to have a relatively high bar to entry, resulting in a lot of quiet or empty zoom rooms. And while the virtual format allows extremely quick hopping between different livestreams, it also seemed to decrease interaction, and, at least for me, it was often hard to focus on the talks. Still, I have to thank the organizing committee for the stellar infrastructure created within a short period of time that offered many avenues for networking, discussion and learning. I had some great discussions during the Q&A sessions!

Before going into this trip report, I should note that I have my own interests, in particular generative modeling and neuro-symbolic AI. It is easy to think these topics are very popular considering how many papers ICML had on these topics, but such trends can be discovered for practically any topic given the large volume of papers!

My highlights of ICML 2020 were certainly the great tutorials and workshops. The tutorial on Bayesian Deep Learning by Andrew Wilson was very insightful, and the workshops were varied and relevant, with two whole days focused on Graph Neural Network (GNN) approaches. If there is one real trend to spot, then it is the increased focus on adding prior knowledge through for example inductive bias instead of blindly applying NNs!

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