[mljoe]

>I think an important lesson for any grad student is to learn to read through the bullshit in papers and try and understand what the authors actually did.

We actively work to make our writing hard to understand in this field. I do this all the time myself. I don't really need this complex looking equation to make my point. But if I don't have it in there a reviewer will think my writing is not academic enough. So there you have it. Once you go in realizing this is the case everywhere, it becomes a lot easier to understand academic papers.

[asadlionpk]

I get your point but I wish this wasn't the case with most research. I, like the author, am not a math guy but have been reading tons of ML papers recently. I usually skip the formal definition parts and get to the 'juicy' implementation parts.

I wish there was a ELI5 section in each paper.

[skiman10]

What have been your favorite papers so far?

[asadlionpk]

That's hard as I haven't read too many. The recent deepmind papers (the ones about imagination) were good. The papers were pretty standard but they came along with explanatory blogpost[1] and some videos covered them too[2][3][4]. This supplementary content is what made them accessible for me.

[1] https://deepmind.com/blog/agents-imagine-and-plan/ [2] https://www.youtube.com/watch?v=xp-YOPcjkFw [3] https://www.youtube.com/watch?v=agXIYMCICcc [4] https://www.youtube.com/watch?v=56GW1IlWgMg