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Earlier quoted context omitted.
At least Ray has the decency to be listed last on the author list! Personally I think the idea of this paper is pretty good, but the evaluation is weak.
> At least Ray has the decency to be listed last on the author list! Just do it like in mathematics: Authors in alphabetical order.
“We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption…
They made a way to take any sentence, and output a small array of numbers that represent its essence. You can use their model to find the essence of your own sentences. And then use it either directly (e.g. compare the essence of two sentences to see if they're saying roughly the same thing) or use it as a starting point for the model you need (e.g. if you're building a system to convert English sentences into French…
Earlier quoted context omitted.
At least Ray has the decency to be listed last on the author list! Personally I think the idea of this paper is pretty good, but the evaluation is weak.
> At least Ray has the decency to be listed last on the author list! Just do it like in mathematics: Authors in alphabetical order.
At least that’s how it is in (psychology and other?) PhD programs.
So Ray may only be supervising or contributing a small portion and is likely listed on all papers his team publishes.
1. This is more Technical Report worthy than paper worthy... 2. "by Ray Kurzweil's Team", although accurate I find that fetishization of certain stars to pretty insulting to the other authors, we already have a convention and it's "Cer et al. (2018)"
At least Ray has the decency to be listed last on the author list! Personally I think the idea of this paper is pretty good, but the evaluation is weak.
>Our pre-trained sentence encoding models are made freely available for download and on TF Hub. what is tf hub? I assume it stands for tensor flow hub but what is that
It looks like an internal site, this is the link it is referring to: https://tfhub.dev/google/universal-sentence-encoder/1
Earlier quoted context omitted.
They made a way to take any sentence, and output a small array of numbers that represent its essence. You can use their model to find the essence of your own sentences. And then use it either directly (e.g. compare the essence of two sentences to see if they're saying roughly the same thing) or use it as a starting point for the model you need (e.g. if you're building a system to convert English sentences into French…
What do you mean by "its essence"? Is this a semantic essence?
Earlier quoted context omitted.
> At least Ray has the decency to be listed last on the author list! Just do it like in mathematics: Authors in alphabetical order.
Usually the actual lead author is first, the assistant authors follow, and the advisor is listed last. At least that’s how it is in (psychology and other?) PhD programs. So Ray may only be supervising or contributing a small portion and is likely listed on all papers his team publishes.
>Our pre-trained sentence encoding models are made freely available for download and on TF Hub. what is tf hub? I assume it stands for tensor flow hub but what is that
Earlier quoted context omitted.
It looks like an internal site, this is the link it is referring to: https://tfhub.dev/google/universal-sentence-encoder/1
https://www.tensorflow.org/hub/modules/google/universal-sent...
Interesting. There's a big need for better vector representations of things in-between words (for which Word2Vec/Glove/FastText work well) and documents (which to me seems impossible. Yes I know about Doc2Vec etc, but really.. it works ok for paragraphs). Facebook's InferSent[1] has worked reasonably well for me for a variety of sentence level tasks, but I don't have anything I can point to to say that it is really s…
From Wikipedia: "Raymond "Ray" Kurzweil (/ˈkɜːrzwaɪl/ KURZ-wyl; born February 12, 1948) is an American author, computer scientist, inventor and futurist. Aside from futurism, he is involved in fields such as optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments.... Kurzweil was the principal inventor of... the first print-to-speech reading machine for the blind,[3] the first commercial text-to-speech synthesizer,[4]... and the first commercially marketed large-vocabulary speech recognition."
He's been in the general space of NLP for quite a while.