Hi all,

I’m currently interviewing for positions as either research scientist or SWE with a couple of big N companies. I am finishing a PhD in an ML related subject, and what I would really like is to find a job where I can do interesting applied research and maybe publish the occasional paper, but in industry rather than academia. However I have no previous experience with the tech industry and so I am flying a bit blind, applying to companies that have large ML teams and hoping to get lucky.

My question is how do you make it into those interesting teams, is it the same process as for generalist new grad roles? As far as the recruiter at Google and FB told me, if I pass the interview and accept the offer I will be matched with a team and can put down some preferences then, but how much leverage do I really have? Do the majority of PhDs just end up on product related teams? Does it depend on the office, what projects are going on at the moment, and if so how easy is it to transfer to research oriented teams later on?

Thanks!

Hello everyone,

I am trying to know more about AI edge computing and how it can be useful for robotic manipulators in next-generation factories. I hope we can you can share from your insight and experience about such a topic.

Thanks

This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

/u/cafedude: https://arxiv.org/pdf/1911.13299.pdf

/u/nivter: On Mutual Information Maximization for Representation Learning: https://arxiv.org/abs/1907.13625

The authors ran experiments to show that MI maximization between two representations is not directly tied to learning good representations. They did so by maximizing MI while also adversarially training the model to perform badly on linear classification models. One key takeaway for me was that encoders that learn good representations tend to ignore unwanted information as a result of which they are hard to invert (high condition number of Jacobian of output wrt inputs)

Besides that, there are no rules, have fun.

My approcah on why we should not care about deepfakes in the elections and why this negative hype does not bring anything good to the tech.

[Why you should not actually care about deepfake?

](http://alexuribarri.com/why-you-should-not-actually-care-about-deepfake.html)

Hi all,

I’m trying to build an auto-encoder with an activity regularization that is a function of the output of the layer for a single observation. As i understand it, activity regularization is usually done over the output of a cell for each batch, to promote balanced and sparse activation of each cell. Is that correct?

In this particular case, i want to promote a sparse activation of the layer in such a way that 1/ cells activation influence each others 2/ the mean activation per cell will not be necessarily balanced . I’m aware that if i’m using the L1 norm, the axis over which the sum is done doesn’t matter, so i plan to use a tweaked L(1/2) norm.

Does this make sense, and is there a simple way to do this in Keras?

Thanks

PS : so far i’ve done something in that spirit by alternatively training the encoder on its own modified output (like putting the maximum activation to 1 and the rest to 0, pretty brutal) and the autoencoder on the training data. kind of works, but it’s slow and could be a lot better

David Duvenaud of the University of Toronto provides an interesting research retrospective on Neural Ordinary Differential Equations as part of the Retrospectives Workshop @ NeurIPS 2019.

Talk https://www.youtube.com/watch?v=YZ-_E7A3V2w

In my DCGAN I have 3 hidden layers for both G and D. Previously I would generate images of 64×64 but decided to double that for 128 x 128. I went ahead and also doubled the inputs and outputs of both D and G.

HOWEVER

On the first epoch the Discriminator became exremely weak and is not able to distinguish any of the generated images from fake. Could of any of you neural net boys guide me to a place where I can find out how to balance them out?

nucleus7 – library for exchangeable and reproducible development of Deep Learning models built on top of tensorflow!

https://www.linkedin.com/feed/update/urn:li:activity:6610662409046642688/