arunsupe

4 ms·

1.
by arunsupe 1y ago
Very interesting list of deceptive influences. Thanks for sharing.
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by arunsupe 1y ago
Thanks for your comments. I am finding myself getting sucked into the decisiveness and click baiting nowadays. Want to implement some of these counter measures. Here are my thoughts: 1. DDG or searxng for search YouTube only via the search
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Ask HN: Where are we vulnerable to exploitation online these days?
3 points by arunsupe 1y ago|5 comments
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Show HN: Zip-sizer – A tool to estimate the compressed size of large archives(github.com)
3 points by arunsupe 2y ago|0 comments
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by arunsupe 2y ago
Hehe. You are right. There are many leftist media outlets. But my point was not to be partisan; just to note that there will be lots of news to cover. Trump is a controversial figure. Every news outlet will profit. Now is not the time to do
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by arunsupe 2y ago
Not sure why. Criticizing the republican (especially Trump) government will be hugely profitable - just under 50% of the US population (175M people) is looking for such criticism, and there is no one else to do it. Every day there will be m
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by arunsupe 2y ago
My question was tongue-in-cheek. I feel the Nobel committee, like every other organization, is also trying to stay relevant and topical. Hard to do this by giving out prizes to old scientists from obscure fields, no matter how important the
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by arunsupe 2y ago
:-)
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Ask HN: How long until a social media influencer wins a Nobel Prize?
3 points by arunsupe 2y ago|6 comments
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by arunsupe 2y ago
Seems intuitive, but is there any evidence to support it?
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by arunsupe 2y ago
Great post. It's nice that these rules can be trivially demonstrated by simulation. The simulation (and visuals) helps validate the concepts.
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by arunsupe 2y ago
Added support for multiple languages, using fasttext's embeddings
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by arunsupe 2y ago
Correct.
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by arunsupe 2y ago
I assumed you meant computer languages :-) If you mean human languages, yes Google publishes word2vec embeddings in many different human languages. Not sure though how easy it is to download.
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by arunsupe 2y ago
This implementation does not because the query has to be a word. One way to extend it to phrases is to average the vectors of each word in the phrase. Another way is to have a word2vec model that embeds phrases. (The large Google News model
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by arunsupe 2y ago
I would think translation to other languages would be trivial. The model is just a map of word to vector. Every word is converted to it's vector representation; then the query word is compared to the input words using cosine similarity
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by arunsupe 2y ago
The model in Google drive is the official model from Google and will work. Haven't tried the huggingface model, but, looks very different. Unlikely to work.
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by arunsupe 2y ago
Oh.Please explain. The example was entirely arbitrary. Should I change it?
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Show HN: Semantic Grep – A Word2Vec-powered search tool(github.com)
356 points by arunsupe 2y ago|57 comments
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Semantic-grep – NLP-powered text search in Go(github.com)
2 points by arunsupe 2y ago|1 comments
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by arunsupe 2y ago
I've built a command-line tool called semantic-grep for searching free-text English content using natural language queries. Some key features: Uses word2vec embeddings to find semantically similar text Written in Go, using only the sta
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A heuristic algorithm for factoring larger numbers using machine learning(carelesslearner.blogspot.com)
7 points by arunsupe 12y ago|2 comments
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The intrinsic value of chess pieces inferred from analysis of 4.6M boards(carelesslearner.blogspot.com)
2 points by arunsupe 12y ago|0 comments
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Machine learning algorithms to intelligently mine Bitcoins(carelesslearner.blogspot.com)
2 points by arunsupe 12y ago|0 comments