Interesting as I have not had much trouble reproducing the results from papers I use as baselines. I find that sometimes, weight initialisation can make a difference so read the paper carefully on how they initialised the convolutional layer weights and check that DGL is using the same method. If not, do a custom initialisation based on the paper.

I find the plenary/invited speaker sessions to always be good value as you get to hear from the top researchers. Second best, in my opinion, are tutorials although it depends on how well organised they are. Workshops are great if you are presenting a paper because these days they are like small conferences and the audience is better targeted so your work is exposed to just the right people. The main conference is good for finding out what the community thinks are the best works for the moment. But usually the papers cover a wide breadth of topics so most might be of little interest and attending the presentations a waste of time; just look at the schedule and go to those presentations you care about.

I guess, you should also try to socialise and meet new people. I'm not good at socialising so for me this has always been the most uninteresting/difficult part of conference attendance.

A somewhat useful guide, How to keep up with machine learning research.

The reviews are out.