0101111101

6 ms·

1.
by 0101111101 1y ago
Amazing!
2.
by 0101111101 1y ago
True, but similarly if your embeddings are any good they'll capture interesting associations between authors, topics and your search query. If you find any interesting author overlap results I'd be very interested!
3.
by 0101111101 1y ago
One chunk embedded together
4.
by 0101111101 1y ago
Exactly! A near property of the matryoshka embeddings is that you can compute a low dimension embedding similarity really fast and then refine afterwards.
5.
by 0101111101 1y ago
That's a nice idea! Might take a look this weekend!
6.
by 0101111101 1y ago
Thanks!
7.
by 0101111101 1y ago
Looks cool! You can input either a search query or a paper URL on arxiv xplorer. You can even combine paper URLs to search for combinations of ideas by putting + or - before the URL, like `+ 2501.12948 + 1712.01815`
8.
by 0101111101 1y ago
Sadly I couldn't find a public API for chemrxiv, but would be happy to be proven wrong!
9.
by 0101111101 1y ago
Do they have a public API/dataset?
10.
by 0101111101 1y ago
Sure! I first used openai embeddings on all the paper titles, abstracts and authors. When a user submits a search query, I embed the query, find the closest matching papers and return those results. Nothing too fancy involved! I'm also
11.
Semantic search engine for ArXiv, biorxiv and medrxiv(arxivxplorer.com)
149 points by 0101111101 1y ago|31 comments
12.
Infinitely scroll ArXiv, BioRxiv and MedRxiv papers(scroll.arxivxplorer.com)
2 points by 0101111101 1y ago|0 comments
13.
A Short Introduction to RLHF(ttumiel.com)
2 points by 0101111101 2y ago|0 comments
14.
Replacing BatchNorm(ttumiel.github.io)
3 points by 0101111101 6y ago|0 comments
15.
Show HN: Interpret Convolutional Neural Networks in PyTorch(github.com)
2 points by 0101111101 6y ago|0 comments
16.
From MobileNet to EfficientNet: With Code(ttumiel.github.io)
1 points by 0101111101 6y ago|0 comments
17.
Interpreting Deep Learning Models(ttumiel.github.io)
1 points by 0101111101 7y ago|0 comments
18.
CS224N: Natural Language Processing with Deep Learning 2019(web.stanford.edu)
1 points by 0101111101 7y ago|0 comments
19.
by 0101111101 8y ago
Tensorflow also created Eager - their dynamic environment
20.
Fast In-Browser Inference with ONNX.js, WebAssembly and WebGL(github.com)
4 points by 0101111101 8y ago|0 comments
21.
The Problem with Jeff Bezos’s $2B Gift to Charity(vox.com)
4 points by 0101111101 8y ago|2 comments
22.
by 0101111101 8y ago
Yeah I think it's important to be completely open about it since browser mining has a bad rep. Everyone trying it should know what happens as much as possible before just clicking 'mine'
23.
by 0101111101 8y ago
Hey HN! Would love to hear any feedback on this project! Positive or negative
24.
Show HN: Mined for Change – In-Browser Crypto Mining for Effective Altruism(minedforchange.org)
3 points by 0101111101 8y ago|3 comments
25.
Fastai Introduction to Machine Learning(course.fast.ai)
2 points by 0101111101 8y ago|0 comments
26.
Fastai Introduction to Machine Learning(course.fast.ai)
1 points by 0101111101 8y ago|0 comments
27.
Algorithms Implemented in Python(github.com)
153 points by 0101111101 8y ago|27 comments
28.
Awesome Lists – Curated Lists of Just About Everything(github.com)
2 points by 0101111101 8y ago|0 comments