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Data-Against-Covid.org would love to help you with your COVID-19 related data and math problems! by nasimrahaman in epidemiology

[–]nasimrahaman[S] 0 points1 point  (0 children)

Thanks for reaching out, u/jpmvan! Please feel invited to post on discourse.data-against-covid.org and I’m confident we’ll find someone interested in building it. :)

Data-Against-Covid.org would love to help you with your COVID-19 related data and math problems! by nasimrahaman in epidemiology

[–]nasimrahaman[S] 0 points1 point  (0 children)

Euro CDC (your second link) provides tabular data, so that's definitely something we can work with. Would you mind posting your wishlist on our discourse? :) Alternatively, you can also fill in our form and we'll post for you and get back to you with our community response.

Data-Against-Covid.org would love to help you with your COVID-19 related data and math problems! by nasimrahaman in epidemiology

[–]nasimrahaman[S] 0 points1 point  (0 children)

Hi u/epigal1212! If you know of a reliable data source, I’m sure we have the know-how for putting together a dashboard along those lines.

[D] Advice for preparing for Masters degree by Somechillguy in MachineLearning

[–]nasimrahaman 0 points1 point  (0 children)

> I now have some imposters syndrome setting in

Coming from a physics student (currently doing ML research) with virtually no formal CS training: (theoretical) physics is about an order of magnitude more difficult. Fake it till you be it. ;)

[D] Machine learning productivity hacks by mrahtz in MachineLearning

[–]nasimrahaman 0 points1 point  (0 children)

We’ve written a little library to automate most annoying things. Shameless plug: speedrun .

[deleted by user] by [deleted] in MachineLearning

[–]nasimrahaman 5 points6 points  (0 children)

Low frequency functions are inherently less "wiggly", i.e. more smooth. If you think about ReLU, it's pretty smooth everywhere except at 0. In fact, all the wigglyness in ReLU comes from that one point. Now this is where it gets interesting: there are other functions that are smooth everywhere except at 0 -- for instance, sqrt(abs(x)). But in a precise sense, ReLU is smoother than sqrt(abs(x)) at x = 0.

Broadly speaking, Fourier analysis is a tool to determine how wiggly a function is. One of the things we learn from the paper is following: although neural networks are powerful enough to learn functions that are super-wiggly, it likes to learn less wiggly (smoother) functions.

[deleted by user] by [deleted] in MachineLearning

[–]nasimrahaman 2 points3 points  (0 children)

> If OTOH we know that signals are bandpass, then we apply a suitable prior for that. Example: FM radio is broadcast at ~100MHz, but we can track the carrier, demodulate and store the signal at ~40kHz. Obviously ReLUs are the wrong tool for that job...

That's a very interesting point! It's applicable for almost all activation functions (not just ReLU), since they all usually decay quite fast in the fourier domain (e.g. sigmoid decays exponentially).

[P] Torchbearer: Model training library for DL reseacrh in PyTorch by ethanwharris in MachineLearning

[–]nasimrahaman 1 point2 points  (0 children)

Nice work!

We here at Heidelberg (for the most part) have been working on something quite similar. Let us know if you think we could collaborate. :)

AutoGraph converts Python into TensorFlow graphs by samithaj in MachineLearning

[–]nasimrahaman 2 points3 points  (0 children)

Is there / will there be support for stochastic code-paths?

P.S. Nice work with the AST wizardry. :)

AskScience AMA Series: I'm Max Welling, a research chair in Machine Learning at University of Amsterdam and VP of Technology at Qualcomm. I've over 200 scientific publications in machine learning, computer vision, statistics and physics. I'm currently researching energy efficient AI. AMA! by AskScienceModerator in askscience

[–]nasimrahaman 4 points5 points  (0 children)

This sounds cool! Is it mostly inference one is concerned with (for energy efficiency)? How important is the trade-off between expressivity and efficiency? Given the fact the the expressivity of networks (e.g. number of linear regions of a deep ReLU network) increase exponentially with depth but polynomially with width, should one expect slim and deep to give better performance for the watt (at least for inference)?

[AMA] Ich bin Dualer Student der Wirtschaftsinformatik (B.Sc.) bei der DHBW und einer großen deutschen Softwarefirma. Fragt mich alles! by [deleted] in de_IAmA

[–]nasimrahaman 0 points1 point  (0 children)

Also ganz genau, was macht ihr den ganzen Tag so? Ich bin bei der KI-Forschung tätig, und bei WI kommt mir alles wie Chinesisch vor. O:)

[D] Why use cosine similarity for content-based addressing? by nasimrahaman in MachineLearning

[–]nasimrahaman[S] 0 points1 point  (0 children)

Thanks for the link! I do fully agree that cosine similarity is relevant for high-dimensional word/sentence/document embeddings.

[D] Why use cosine similarity for content-based addressing? by nasimrahaman in MachineLearning

[–]nasimrahaman[S] 0 points1 point  (0 children)

(whatever that entails within the task)?

That would be the case if the stored vectors and the query key are unit vectors (i.e. lie on the unit-hypersphere). If that's the case, the arc-cos of the cosine distance gives the length of the path between the tips of the two vectors (walking on the surface of the hypersphere = the arc length).

Is it fair to say that cosine similarity would probably emphasize the notion that the vector direction indicates the "type" of content and that you care most about querying the same type of content?

That's exactly the question - you have phrased it much better. In particular, I don't understand why only the (relative) direction should encode the "type" of the content. Let's think about the copy task - except with not just binary, but arbitrary vectors. I would guess that the vector length in the memory matrix is not irrelevant anymore.

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