HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
lixin4ever
Conference-Acceptance-Rate
Jupyter Notebook2022–2025
lixin4ever / Conference-Acceptance-Rate

Conference-Acceptance-Rate

Acceptance rates for the major AI conferences

2Distinct people
2Mentions
0Explicit recommenders
4.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Acceptance rates for the major AI conferences

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year2 dated mentions

Observed mentions: 2022-08-26 15:57:22 UTC to 2025-06-04 08:33:27 UTC.

012022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

2 sources
> shouldn’t we expect a high quality journal to retract often as we gather more information? This is complicated, and kinda sad tbh. But no. You need to carefully think about what "high quality journal" means. Typically it is based on something called Impact Factor[0]. Impact factor is judged by the number of citations a journal has received in the last 2 years. It sounds good on paper, but I think if you think about it for a second you'll notice there's a positive feedback loop. There's also no incentive that it is actually correct. For example, a false paper can often get cited far more than a true paper. This is because when you write the academic version of "XYZ is a fucking idiot, and here's why" you cite their paper. It's good to put their bullshit down, but it can also just end up being Streisand effect-like. Journal is happy with
Read the rest
its citations. Both people published in them. They benefit from both directions. You keep the bad paper up for the record and because as long as the authors were actually acting in good faith, you don't actually want to take it down. The problem is... how do you know? Another weird factor used is Acceptance Rates. This again sounds nice at first. You don't want a journal publishing just anything, right?[1] The problem comes when these actually become targets (which they are). Many of the ML conferences target about 25% acceptance rate[2]. It fluctuates year to year. It should, right? Some years are just better science than other years. Good paper hits that changes things and the next year should have a boom! But that's not the level of fluctuation we're talking about. If you look at the actual number of papers accepted in that repo you'll see a disproportionate number of accepted papers ending in a 0 or 5. Then you see the 1 and 6, which is a paper being squeezed in, often for political reasons. Here, I did the first 2 tables for you. You'll see that has a very disproportionate ending of 1 and 6 and CV loves 0,1,3 These numbers should convince you that this is not a random process, though they should not convince you it is all funny business (much harder to prove). But it is at least enough to be suspicious and encourage you to dig in more. There's a lot that's fucked up about the publishing system and academia. Lots of politics, lots of restricted research directions, lots of stupid. But also don't confuse this for people acting in bad faith or lying. Sure, that happens. But most people are trying to do good and very few people in academia are blatantly publishing bullshit. It's just that everything gets political. And by political I don't mean government politics, I mean the same bullshit office politics. We're not immune from that same bullshit and it happens for exactly the same reasons. It just gets messier because if you think it is hard to measure the output of an employee, try to measure the output of people who's entire job it is to create things that no one has ever thought of before. It's sure going to look like they're doing a whole lot of nothing. So I'll just leave you with this (it'll explain [1]) As a working scientist, Mervin Kelly (Director of Bell Labs (1925-1959)) understood the golden rule, "How do you manage genius? You don't." https://1517.substack.com/p/why-bell-labs-worked There's more complexity like how we aren't good at pushing out frauds and stuff but if you want that I'll save it for another comment. [0] https://en.wikipedia.org/wiki/Impact_factor [1] Actually I do. As long as it isn't obviously wrong, plagiarized, or falsified, then I want that published. You did work, you communicated it, now I want it to get out into the public so that it can be peer reviewed. I don't mean a journal's laughable version of peer review (3-4 unpaid people that don't study your niche and are more concerned with if it is "novel" or "impactful" quickly reading your paper and you're one of 4 on their desk they need to do this week. It's incredibly subjective and high impact papers (like Nobel Price winning papers) routinely get rejected). Peer review is the process of other researchers replicating your work, building on it, and/or countering it. Those are just new papers... [2] https://github.com/lixin4ever/Conference-Acceptance-Rate [3] https://imgur.com/a/YefOcuA
In Deep learning gets the glory, deep fact checking gets ignored
I saw a list of acceptance rate here: https://github.com/lixin4ever/Conference-Acceptance-Rate. Is 18% or so acceptance rate really low, though? Almost 2 in 10 submissions are accepted, and I thought "the top" meant something like 2% or less. BTW, is there any resources that catalogs which ideas in papers may work well in industry? As someone outside of academia, I find there are simply too many papers, even from top conferences, for me to consume. It's hard for me to know which paper's ideas can help me or not, and this 18% acceptance rate is not a good enough filter any more.
In The culture of rejection in computer science publications

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.