31–40 of 42 posts
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.
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.
After reading your comment, I now feel embarrassed as to why I haven't heard of this site.
Question - I get that your run-of-the-mill paper saying "Here we present a novel algorithm for xyz" will usually have the algorithm defined in simple psuedo-code, maybe with an implementation in a "real" language as a proof of concept. But for the many papers describing novel ML models, how does that work? They seem to use images that diagram out the different layers of the model. But is that truly "universal" the wa…
The longer answer is that the rest of the Conv2D configuration can then be easily overlooked, unless changed from the defaults. And those can be different across frameworks and potentially break things, even they even exist in your preferred framework. You can always create custom layers though, if needed.
But many papers also seem to do a bad job describing the actual structure of their own ML network. They can be vague, confusing, or simply inaccurate. And that can be because they are a general concept with flexible details, or because they struggle to put their model into clear words and diagrams. Or simply because they know the code is going to do the lifting.
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.
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 be…
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.
Question - I get that your run-of-the-mill paper saying "Here we present a novel algorithm for xyz" will usually have the algorithm defined in simple psuedo-code, maybe with an implementation in a "real" language as a proof of concept. But for the many papers describing novel ML models, how does that work? They seem to use images that diagram out the different layers of the model. But is that truly "universal" the wa…
My experience has been that pseudo-code is anything but universal.
In fact, having had many times to implement actual working code from research papers pseudo-code, I would posit that pseudo-code is nothing but a license for academics to provide stuff that simply doesn't work to the reader and get away with it. Thanks to pseudo-code, they get to gently skip over the hard bits to get the paper out the door as quickly as possible.
Papers with actual, git-clonable, working code, should be the standard for CS academic publishing.
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.
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 be…
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.
I implemented Wang–Müller algorithm, described it, and embedded the code to the pdf, along with tooling how to generate the example diagrams of the paper (and the whole paper). Everything is in the pdf[1]. Arxiv.org won't accept a pdf with attachments though, so only a stripped-down version will come there (once/if I get an endorsement, fingers crossed). I copied this concept from Joe Armstrong, where he suggested to…
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.
“Bread and butter” for me is http://arxiv-sanity.com
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.
I think you overestimate how many HN readers are "Research Engineers and Scientists working in Deep Learning".
Transformer based architectures and unsupervised pre-training are achieving state of the art results across multiple modalities including NLP, CV, speech recognition, genomics, physics etc - so here's my must read list of recent papers on the topics (along with some of my notes). Happy holidays! [1] Attention Is All You Need (2017) https://paperswithcode.com/paper/attention-is-all-you-need Introduced the Transformer…