Hi everyone! As a student in The Netherlands, I’m doing a graduate research on Amazon recommendations’ influence on purchase choice of Amazon users. If you occasionally order from Amazon or know people who do, please fill in this short questionnaire / share this with your friends/family/community.
This research may produce some interesting conclusions about significance of AI in marketing, which is why I’m really excited about it and would like to get as much data as possible.

Thank you !

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Survey link: https://erasmusuniversity.eu.qualtrics.com/jfe/form/SV_0jOW3i5nxkVALT7

Out of curiosity, does anyone know of any results that train deep models on the ImageNet dataset —

• in an online (i.e. one-pass) fashion; and/or
• multi-pass but without any data augmentation?

Think it would be an interesting exploration understanding online vs. batch training in nonconvex models!

(Of course, one should take efforts to push for the best results in both settings, such that those results are comparable.)

A short blog post discussing Stochastic Weight Averaging and the Ornstein-Uhlenbeck Process. We discuss why SGD is not able to position itself in the center of flat-wide minima but instead positions itself near the boundary of the minima.

Hi folks,

some time ago I did some research about how to classify dialogs with recurrent neural networks (GRUs, LSTM) and also 1DConvolutions. I found an architecture, which is quite new and worked pretty good on a data, which is not public. I wanted to publish it as a paper, but I think it will not happen anymore. First there is no appropiate public data set available for this task and second I just don’t have enough time at the moment.

Anyway I published details on my blog, if you are interested, take a look here.

Cheers,

Currently leading alone the ML related solutions in a small AI startup that is being acquired. Was fortunate to be part of researching and deploying solutions I’m very proud of – it was an amazing environment but the purchasing company is a large successful S&P500 far from being information/data-driven. Also, it doesn’t seem to have a healthy, functioning ML team that I can join.

They gave me a generous offer, but I fear of losing the learning momentum and professional development within the corporate area and organizational politics. I don’t want to just rest comfortably waiting for my yearly retention bonus, but very eager to learn and develop.

What is your experience with similar cases? Would love to learn more from your perspective. Specifically, not sure if I should stay or jump ship to another startup.

As we know, the users in recommendation system are different, the new vs the regular, the active vs the inactive, etc. Is there any good method to fit this situation. I have some idea, but not smooth。 （1）construct multiple model for different people, waste much computation resource (2) use the obvious difference as feature (embedding look up). but this method may use a continual space to simulate the discrete space (different user may have totally different distribution) Does anybody have better idea?