If you're looking for a "menu" of papers from various subfields, I've been collecting a list of all best paper awards from a set of 30 computer science conferences for the past 25 years. This is just a personal interest of mine, and is a long page with no ads or upsell.
These are papers that were deemed "best papers" in that year, though obviously may not have turned out to be as influential in retrospect, i.e. they're likely not the papers we consider "best" when looking back today.
Tbh I always direct people who want to read old important papers to lecture notes or textbooks, at least in distributed algorithms or CS theory.
I've found that the original papers are always super dense and the material has usually evolved to be more explainable, especially when someone has put in the time to compile it as part of a course.
any recommendations? I read the Cormen introduction to algorithms 3rd ed book, and then a whole bunch of machine learning related books, but I always feel there is a world of algorithms out there that are pretty neat that I haven't uncovered yet.
The 4th edition of Cormen book got published recently! Now in colour! I think it added a bit to better comprehension. It is slightly thinner than 3rd edition as they removed computational geometry algorithms and added some ML stuff.
It's taking a while because I go off on tangents - spent ages looking at the Mother of all Demos and its offshoots. Lately I watched everything I could find by Bret Victor...
Yeah, I think test-of-time awards are a better indicator of which papers actually had solid impact; there's often very little overlap b/w best paper awards and test-of-time awards.
Wow. I am so glad to see you here. I have been following your webpage for the last decade perhaps. Thank you for putting those paper references. (Also, on a different note how many different Jeff Huangs have contacted you so far?)
Thanks, I guess you're referring to my page from about 12 years ago when I offered to give emails to other people with my name? I think 5-6 other people reached out and I gave them emails that are still running now. I know one other Jeff Huang who I'm pretty friendly with (and we've had random encounters in person), and the others are strangers.
>Thanks, I guess you're referring to my page from about 12 years ago when I offered to give emails to other people with my name?
Yep. I didn't realize so much has changed with the rest of the site There was a bit of slowdown I presume in mid-2010s where the table felt a tad behind schedule. I did think of personally writing you an email to help in updating if necessary. In fact, I secretly wanted to just mirror your publication page - but stealing someone's thunder wasn't my ballgame :)
That's a great resource, thanks. Looking at the list the paper titles could be be more readable, perhaps bigger and perhaps not in blue. There's a lot of information but you can always scroll.
Question though, have you got a process for reading individual papers? In college my old CS professors insisted on reading the abstract, then the conclusions, then the references, and then the rest of it.
Here's one way (YMMV) of reading academic papers. It usually takes time, and a couple of passes, and you may try to understand the ideas and impact first, to then move to the design/implementation, and the experiments that demonstrate the paper claims. http://ccr.sigcomm.org/online/files/p83-keshavA.pdf
Caveat: my training is in control system,s not computer science. I assume these types of papers to be similar enough that the following will be useful.
I have heard that advice before, but I generally disagree. Unless you are doing a literature search, reading the references is a waste of time. And most conclusions aren't really worth reading either. They typically read as though the author has completely exhausted themselves by writing the rest of the paper, and simply restart the last paragraph of the introduction in new words.
If you are trying to get up to speed on a new field, a good introduction can really help with you reference search.
Part of "how to read a paper" depends on what you are trying to do. If it's a seminal paper that are are just going to read, that is a very different thing from reading a paper in search of a solution (or trying to figure out of your idea is novel).
In this second case, your main task is to decide if the paper is even worth reading. IMO, this takes a lot practice. Fully reading a paper to the extent you really understand it can be extremely time consuming. It's important to be willing to throw out the paper if at any point (no matter how much time you've put into it) it becomes clear it doesn't work for you.
I will generally skim the abstract and/or the last two paragraphs (or so) of the introduction. If it still sounds promising I look through the next section or two, which are usually some kind of "problem setup" and "proposed solution scheme". I skip things I don't immediately understand. If my interest is still piqued, I look for the results section where the plots are (if it's a practical paper). If I'm still interested, I go back to Section II and start reading more carefully. I'll spend more time with tricky math, but not too much. Save staring at the same four equations for two hours for at least the third pass.
Oh, and if a paper is tricky (for me) and seems worth my time, printing it out single sided and laying the pages out side by side on my desk can be really helpful.
About 13 years ago when I started this, I selected what I considered the most well-known broad conferences in each subfield (I'm trying to avoid using the words "top" or "best"), though the list is notably missing SIGGRAPH which didn't have such an award, and architecture conferences which I considered more in ECE than CS.
ACM archives -- not really, I haven't added any new conferences because it makes each year even more work to update. And I find that nearly all CS papers are accessible through various sources that you can find in Google Scholar or Semantic Scholar (e.g. author homepages, course websites, arXiv, etc.).
I’ve seen this list shared before, and want to thank you for sharing it again! I had forgotten about this resource and am pretty stoked to be seeing it
https://jeffhuang.com/best_paper_awards/
These are papers that were deemed "best papers" in that year, though obviously may not have turned out to be as influential in retrospect, i.e. they're likely not the papers we consider "best" when looking back today.