[deleted]

You can pre train and finetune energy efficient language models such as electra or convbert in this gpu. But maybe the batch size might not be too big so the descent would be a bit noisy and also keep the corpus size as small as possible.

Look into bio electra paper which also has the notebook on how he has trained it .

Hi. I'm an international grad student in the US and am looking for an internship for the summer. Please, if you have some tips, or if you care to have a look at my profile, just let me know. Thank you!

I've been wanting to build a python script just to do what you mentioned.

Good GPT-3 bot.

(lmao checkout comment history)

A Snappy Headline Is All You Need

You can fine-tune image generator models and some smaller language models.

You can also do tasks that don't require super large models, like image recognition.

>that's beyond just some toy experiment?

Don't knock toy experiments too much! I'm having a lot of fun trying to build a differentiable neural computer or memory-augmented network in pytorch.

Anyone else getting this error where it directs here:

https://tts.themetavoice.xyz/?error=server_error&error_description=Database+error+saving+new+user

Hello guys I'm new in the machine learning and I wanted to know if a i5 8th gen and a 1060 6 gb paired with 16 Gb of ram are they enough for any type work that could come my way??!

Wow, looking forward to seeing more cool stuff 👍

Thanks for your ideas. Building a paid experience for companies is a great idea, I will consider it.

Category tagging like „computer vision“, „natural language processing“ etc. should be relatively straightforward. Will implement this in the next couple of days :)

More paper specific tags could be generated using GPT-3, I think that would make sense, when the database is a bit larger. Right now, I would guess that most tags would be unique to a single paper.

I left academia in the 1990s. When did paper titles becomes so vague? "In my day", you had a good idea what the paper was about just from the title. Reading the first 30-40 papers here, what are authors trying to do? Be comedians?

I need a more up-to-date buzzword thesaurus of research fields and fashions, so I can interpret the context/semantics of these titles! I feel old 😫

Feedback, any way you could add category tagging to papers? And have links to related tags?

I suspect companies would pay a subscription for this! Individuals no, especially me ;)

Great work, thank you.

https://ai.googleblog.com/2022/11/mixture-of-experts-with-expert-choice.html?m=1

https://ai.googleblog.com/2022/01/scaling-vision-with-sparse-mixture-of.html?m=1

Currently none are available. I am eagerly waiting for them too. https://medium.com/p/c24f1b22a235

Stanford university course taught by Andrew Karpathy. It’s a little older now but I do think it covers important material. You can find it on YouTube

Quick question, what is CS231N? And where is that course taken?

Im fairly certain it’s something with the model. Like even fine tuning is giving these weird errors, when it had no problems for GPT-Neo.

We also ran this stuff on T5, obviously had to configure the rest of the code differently but it was doing fine for that as well.

Thanks. Time series classification is what I was looking for.

Thanks for letting me know about model stacking, I have never done it but will try and see which fits the best.

Cheers!

Ahh this makes a lot of sense. Regarding stage 0, how do you split codes? Like just by lines or have some methods to extract functions and classes?

I wrote some script that allows you to extract Python functions using regex, but this is def not scalable to other languages…

Your arxiv-summary.com project sounds like a great idea and a very useful tool for people in the field of machine learning. It's great that you're using PapersWithCode to filter out the most relevant papers, and that you're using GPT-3 to summarize the papers' sections and subsections.

The fact that the website is able to fetch new papers daily and parse their pdf and LaTeX source code to extract relevant sections and subsection, and then summarize those with GPT-3, it will make it more accessible for people to quickly understand the main ideas and contributions from the abstract.

It's great to hear that you have a search page and an archive page where users can get a chronological overview, this will help people to keep track of new publications in their field.

I wish you the best of luck with your project and I'm sure it will be a great resource for many people in the field of machine learning.

This is golden thank you

[removed]

Dear god how I'd love to see that ran through the SUMMON code. What eldritch furnishings are they walking over? Stumbling upon??