[rg111]

It is weird to see Papers with Code on the front page of HN.

This site is the bread and butter of each Research Engineers and Scientists working in Deep Learning. You use the site almost everyday.

Advanced learners also use the site regularly.

You would just think that "everyone knows" and never think of sharing the site on HN.

[TuringTest]

I'm concerned that this "every knows" is increasingly becoming a true social problem, unsolved by current technology - in fact, worsened by it.

Knowledge about a field transfers best by hands-on association with people who practice it. Before widespread IT, communities of practice were local and relatively homogeneous; so it was easy to share the essentials of a field quickly, and get newcomers up and running with best practices.

Nowadays however, communities of practice are widespread, coming around all the world with very different backgrounds, communicating through low-bandwidth channels, and we're flooded with information so it's difficult to ascertain what is essential and what's accessory.

It is much more difficult for an outsider to grasp the essential qualities of a field they want to enter, as there are usually no guides comprehensive enough to detail everything you need to know.

[jmnicolas]

Why is it a problem? People should put at least a minimum of effort to research what might interests them. Not everything has to be spoon fed to people.

I never found any subject that needed let's say more than 10 minutes of internet searches to know if it's worth pursuing.

It was much harder before the web. I remember as a kid seeing books about C++ in the local shop but even with looking inside not understanding what C++ was. Nowadays I would get my answer almost instantly.

[JohnHaugeland]

> I'm concerned that this "every knows" is increasingly becoming a true social problem, unsolved by current technology - in fact, worsened by it.

You couldn't possibly believe this if you were old enough to remember what preceded the internet.

Good lord, no, today is not worse than microfiche and card catalogs.

[TuringTest]

I was a young adult when the web become widespread, and the problem I'm talking about was milder: precisely because there was a shortage of documentation, what was available limited the number of topics that you could learn about, and being flooded by different sources was less of a problem.

It was still possible to define a Library Science were books were classified by hand, and not some secret algorithm counting links as votes or learning and regurgitating a corpus of loosely related documents without understanding any of it. I.e., it was possible to make sense of information sources, and whatever you learned of a field came with a single consistent narrative. Nowadays, information gathering has become an exercise in picking and choosing unconnected fragments from which you must infer your own understanding.

In some ways you can still try to emulate the old way, by limiting yourself to a small set of publishers who try to compile and organize a small part of a field of knowledge - yet it is much easier than the teachings of that source will be deeply contradicted by some other seemingly authoritative source, without a clear way to know which one should you rely upon, and with the whole exercise feeling like it provides an incomplete perspective.

[fault1]

I think you overestimate how many HN readers are "Research Engineers and Scientists working in Deep Learning".

[p1esk]

He also overestimates the importance of that site for “Research Engineers and Scientists working in Deep Learning".

[visarga]

Let me guess - everyone - means /r/machinelearning and a curated list of people on Twitter?

[p1esk]

I’m a DL researcher, I’ve known of this site for a few years, and while the original motivation behind it was good I personally never extracted much value from it. Usually googling the paper title or a model name plus ‘github’ and/or ‘pytorch’ will produce all relevant links to code.

“Bread and butter” for me is http://arxiv-sanity.com

[criticaltinker]

A couple previous discussions for those interested:

https://news.ycombinator.com/item?id=19054501 (Feb 1, 2019) 411 points, 23 comments

https://news.ycombinator.com/item?id=23391934 (June 2, 2020) 304 points, 21 comments

[ivegotnoaccount]

Not everyone works in machine learning which seems to be the only subject the site handles, and those that do not work in it may still be interested in hearing about the existence of such a website.

[barefeg]

I’m curious, how does it fit your daily workflow as an engineer? Is it somewhere where you get the “news” for the day? Or do you use it for getting information relevant to your current work projects?

[stared]

I never tracked is like news. I used it for two main things:

- Checking the state of the art (SotA) for a given problem. For some problems 2 year old solutions are still close to SotA; in others - there is a huge difference. And if there is a huge difference - is it because of architecture and parameter tuning, or using totally different architectures and training modes.

- Running code - to be used somewhere, or as a reference. Papers never have all details, and do not compile.

Context: I used to work in the field, as a consultant. Though, I cite Papers with Code in one overview paper.

[igorkraw]

You use it to find the code and data of a paper - since it also lists other implementations - to run additional baselines on Imagenet in order to appease reviewer #3( who has no idea why your paper on convex optimization has nothing to do with this but it's easier to run them than argue with them).

Pre-parenthesis part is dead serious, parenthesis part is slightly hyperbolic due to accumulated trauma with bad reviewers

[visarga]

It also gives an overview of the current state of the art for thousands of tasks and indexes current research by methods used, so you can quickly bootstrap research on a topic.

Another great resource is the HuggingFace model zoo. So many trained models easy to deploy.

[MasterIdiot]

While there's probably some value in posting the resources "everyone knows", Papers with Code was submitted multiple times in the past few years, which is a pretty common HN thing (whether this behavior is desired is up to dang and the community I guess).

[hungryforcodes]

I've never heard of it. Glad to have made its acquaintance.

[mayankkaizen]

After reading your comment, I now feel embarrassed as to why I haven't heard of this site.

[toxik]

Don’t worry, it’s not true.

[btschaegg]

Obligatory XKCD:

"Ten Thousand"

https://xkcd.com/1053/