For the experts that are keeping up with the everyday ML research breakthroughs could you summarize the best posters/talks to be held at the upcoming NeurIPS 2019? “best” meaning personal best.

If anyone knows of any blog posts on the subject also please post.

My boss wants me to do a hack project where I cluster user feedback / complaints (e.g. people saying “wtf I can’t log in” or “this UI is ugly bla bla” etc.) We have >100k unlabeled data points. There may be jargon in there but it’s mostly legible English. Our goal is to cluster these things so that those talking about the same issue get grouped, and we can take care of them in chunks as nobody wants to read a thousand of these per day.

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I’m not an NLP guy by any stretch, so I’ve been reading papers all day to try catching up, however I’m kind of in the middle of the ocean right now. There’s a lot of stuff out there and being inexperienced I thought I’d summon you folks for a discussion on what to try.

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My idea now is to use some kind of Transformer model to embed each data point (paragraph) but stuck here as I’m learning that the vectors coming out of those encoders don’t cluster well by text meaning. Let me know any ideas.

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P.S. simple models like counting keywords failed me because 1) the data points have a lot of shared vocab so irrelevant things get clustered together, and 2) there are many ways of talking about the same thing with different words.

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Ciao

I was looking for a VM with a GPU to train my model. I was going to use Google Cloud but unfortunately they don’t do business with people from my country so I had to look elsewhere.

That’s when I remembered of paperspace which looked pretty nice. They even have a separate option for ml which allows you send calculations to the cloud and launch notebooks.

But the system wouldn’t accept my card. It simply said “Card is declined”. I reached support and they said that’s probably because their system cannot determine my ip because of VPN or firewall and that I need to turn that off to add card info. Pretty strange thing to ask for IP to simply add payment info but that worked.

I quickly understood that I’m not comfortable with this Gradient service and that I’d like to operate from PyCharm, using vm as a remote interpreter via ssh.

So I tried to rent a regular VM but all the options were locked saying that I need to send a request, describing reasons and ways in which I want to use it. Strange, but I send a request, saying that thing about using PyCharm. Waited a day, no response and sent one more request.

Later that day I get an email from their security staff saying that my account rated highly on their risk matrix and was flagged as suspicious and that I must send them:

• a photo of my ID with name matching the card
• contact information
• company or personal website
• link to github or social media accounts
• detailed description of what I’m going to do with the service

And if I don’t do it in 24 hours they will ban me forever.

tl;dr accused me of being suspicious and potentially fraudulent and asked all kinds of personal info to unblock me

Well, imo they should balance their false positive rate and improve customer service greatly.

What are other good alternatives for VMs for machine learning? What do you use?

For LeNet trained on MNIST with the lowest possible loss (global minima),

• What would the test error rate look like? Is there a benchmark for best possible performance?
• Can we achieve global minima on non-convex loss functions for a classification task with a minimum number of parameters? Or conversely, how does adding more parameters to a NN help with this?

I see a lot of brilliant techniques that produce incredible results, but are there any techniques that try to bring learning speeds an order of magnitude or two up even if it costs them half the accuracy, or techniques that try to learn from 400 images instead of 40k?

Or in other words, I would love if someone were to link me some research pursuing non-conventional goals.

I started a project where I’m going to shoot short stories generated by GPT-2. I insert the first line and it completes it. Of course, there is a lot of filling the gaps as sometimes all we get is dialogue and other times only a setting.

This is my first video and I’ve got a few others already shot and plenty scripts selected.

https://www.youtube.com/watch?v=QfI0Pu0jz3E

I think this is an interesting application of GPT-2 and illustrates visually that although there is coherence in the text, meaning is many times lost in the process.

It would be amazing if in a few years after posting consistently we can see the evolution of AI generated scripts and who knows we can actually have an interesting story which is not pure absurd comedy as they are mostly now.

Hope you enjoy the project and, if you do, please suggest starting lines.

Hi all,

I applied kmeans clustering to some of the Pollock’s paintings. The idea was to track the artist’s usage of #colors through the years. Here’s the outcome!

I had really good fun in mixing computer science and art. I used Python with the standard data science stack (pandas, numpy, scikitlearn) plus opencv. echarts for the visualizations at the end of the article .

Let me know what you think!

https://medium.com/@andrea.ialenti/clustering-pollock-1ec24c9cf447

https://nostalgebraist.tumblr.com/post/189464877164/attention-conservation-notice-machine-learning

Originally saw this on Hackernews

https://news.ycombinator.com/item?id=21710863

Are things really this bad? Isn’t the TF 2.0 API cleaning supposed to make Keras the standard API for TPUs? Why doesn’t he use that?

Edit: also, is this an indictment of TF in general or just TPUs?