Song: https://twitter.com/ai_antihype/status/1155091710281580548?s=20

Bonus song: https://twitter.com/ai_antihype/status/1155220318731919360

Is there a resource to find the best model for a particular task?

What’s the best way to do so? As of now, I usually rely on my uni profs or seniors..

Am I the only one who finds it hard?

How do you folks go about this? What has worked/not worked?

I have seen that they have had impressive results for general CS/coding programs.

I am wondering how they have been doing in ML. I only know of ShapestMinds who is doing this, and I am think InsightAI does this as well.

I’m a third-year undergraduate student in CS, focusing in ML and I’ve been reading a few papers on GANs. I was hoping someone could show me relevant papers on GAN architectures.

In particular, I have the idea that you can create more complicated GAN systems and perhaps get better results. AFAIK, most GANs are structured as a combination of a discriminator and a generator. My thinking is that you could split up the generator into multiple generators and then create an ensemble network, let’s call it a Decider. Ensembles generally train their generators on all available data and then cleverly combine the generators through techniques like boosting. Unlike the traditional ensembles, I want to train the Decider and the multiple generators adversarially. More specifically, the utility function of each generator would seek to maximize the probability it is selected by the Decider. Similarly, the Decider would seek to optimize its correct selection of a generator.

Is anyone aware of multiple adversarial levels in GANs? My google-fu hasn’t been strong enough to find anything related. I’m also wondering if there’s an obvious reason such experiment would fail to produce anything interesting. Any feedback is greatly appreciated.

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/nobodykid23: Feature-wise transformations

Besides that, there are no rules, have fun.

It’s summer. I’m done with my first semester of SE, so let’s talk about ML books that we’re currently studying. My college doesn’t offer ML, nor does it offer any good math classes so if you’re beginner like me, you have to study 24/7 and fail at converting formulae to code so many times you get literally fed up with this crap. I’m studying:

1- My friend’s Computational Linguistics dissertation. I’ve known her for along time, and helped her with Python since she wasn’t a programmer. Now she does NLP better than I do. She does research, and I can’t even understand her dissertation. Partly because it’s in Persian. I used to be an English lit student like her, but I hated English lit. I originally wanted to study CS, but I failed the SATs, so I had to apply to English lit if I wanted to go to a good college. But then I said “fuck it!” and dropped out and applied to community college. Such is life in the third world! Anyways…

2- Foundations of Machine learning by MIT Press. It’s such a great book. It has an unhealthy obsession with PAC though.

3- Introduction to Deep Learning by Eugene Charniak: Oh my God I love this book! So simple! So nifty! It’s obvious it’s been typeset by the same person who did Foundations of et al, because they look practically the same. And both have been published by MIT Press, although Charniak teaches at Brown.

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So what are you studying?